Scientific agencies spend substantial sums producing and improving forecasts of seasonal climate, but they do so without much information about these forecasts’ value in practice. Here we show that financial market participants value the production of seasonal forecasts: options traders price the uncertainty generated by upcoming United States National Oceanic and Atmospheric Administration Winter and El Niño Outlooks. Each outlook affects firms throughout the economy, with total market capitalization of $6 and $13 trillion, respectively. A 1% improvement in the skill of the El Niño Outlook reduces firms’ exposure to a one standard deviation shock by $18 billion and induces traders to spend an additional $2 million hedging the outlook’s news. Firms must not be able to undertake ex-ante adaptation that would eliminate their exposure to the forecasted portion of seasonal climate without imposing substantial costs of its own.
Barsugli, Joseph J., David R Easterling, Derek S Arndt, David A Coates, Thomas L Delworth, Martin P Hoerling, Nathaniel C Johnson, Sarah B Kapnick, Arun Kumar, Kenneth E Kunkel, Carl J Schreck, Russell S Vose, and Tao Zhang, March 2022: Development of a rapid response capability to evaluate causes of extreme temperature and drought events in the United States. Bulletin of the American Meteorological Society, 103(3), DOI:10.1175/BAMS-D-21-0237.1S14-S20.
Research over the past decade has demonstrated that dynamical forecast systems can skillfully predict pan-Arctic sea ice extent (SIE) on the seasonal time scale; however, there have been fewer assessments of prediction skill on user-relevant spatial scales. In this work, we evaluate regional Arctic SIE predictions made with the Forecast-Oriented Low Ocean Resolution (FLOR) and Seamless System for Prediction and Earth System Research (SPEAR_MED) dynamical seasonal forecast systems developed at the NOAA/Geophysical Fluid Dynamics Laboratory. Compared to FLOR, we find that the recently developed SPEAR_MED system displays improved skill in predicting regional detrended SIE anomalies, partially owing to improvements in sea ice concentration (SIC) and thickness (SIT) initial conditions. In both systems, winter SIE is skillfully predicted up to 11 months in advance, whereas summer minimum SIE predictions are limited by the Arctic spring predictability barrier, with typical skill horizons of roughly 4 months. We construct a parsimonious set of simple statistical prediction models to investigate the mechanisms of sea ice predictability in these systems. Three distinct predictability regimes are identified: a summer regime dominated by SIE and SIT anomaly persistence; a winter regime dominated by SIE and upper-ocean heat content (uOHC) anomaly persistence; and a combined regime in the Chukchi Sea, characterized by a trade-off between uOHC-based and SIT-based predictability that occurs as the sea ice edge position evolves seasonally. The combination of regional SIE, SIT, and uOHC predictors is able to reproduce the SIE skill of the dynamical models in nearly all regions, suggesting that these statistical predictors provide a stringent skill benchmark for assessing seasonal sea ice prediction systems.
Light-absorbing impurities (LAIs) deposited on snow surfaces can accelerate melt by increasing solar radiation absorption through snow darkening and grain metamorphism. To improve the predictive capability of the global impact of LAIs on the surface energy balance, we have developed a simple snow parameterization - Snow LAI Redistribution (SLAIR) to estimate the snow albedo based on the concentration of LAIs and grain size. The parameterization can be run as a standalone model constrained by temperature, snowfall, ablation, and aerosol deposition or be implemented into large scale models. The concentration of LAIs at the snow surface depends on aerosol deposition and vertical redistribution during melt. To represent the uncertainties associated with different melting regimes, two approaches were considered, one assuming all the meltwater is contributed from the top of the snowpack (“surface melt mode”) and one assuming each snow layer contributes the same fraction of the mass of the total melt (“uniform melt mode”). The parameterization is evaluated as a standalone model against publicly available data at
the French Alps using observational inputs. The parameterization captured the temporal variations in grain size but not the detailed variabilities. For concentration of LAIs and visible albedo, both melting modes agree reasonably well with observations during the accumulation phase but only the surface melt model reproduced observations with good agreement. Overall, the simple snow parameterization can estimate the near-surface
concentration of LAIs, grain size and visible albedo within a reasonable range. Further developments are required to minimize uncertainties, especially for relatively warm and humid regions.
This study shows that the frequency of North American summertime (June–August) heat extremes is skillfully predicted several months in advance in the newly developed Geophysical Fluid Dynamics Laboratory (GFDL) Seamless System for Prediction and Earth System Research (SPEAR) seasonal forecast system. Using a statistical optimization method, the average predictability time, we identify three large-scale components of the frequency of North American summer heat extremes that are predictable with significant correlation skill. One component, which is related to a secular warming trend, shows a continent-wide increase in the frequency of summer heat extremes and is highly predictable at least 9 months in advance. This trend component is likely a response to external radiative forcing. The second component is largely driven by the sea surface temperatures in the North Pacific and North Atlantic and is significantly correlated with the central U.S. soil moisture. The second component shows largest loadings over the central United States and is significantly predictable 9 months in advance. The third component, which is related to the central Pacific El Niño, displays a dipole structure over North America and is predictable up to 4 months in advance. Potential implications for advancing seasonal predictions of North American summertime heat extremes are discussed.
The Kuroshio Extension (KE), an eastward-flowing jet located in the Pacific western boundary current system, exhibits prominent seasonal-to-decadal variability, which is crucial for understanding climate variations in the northern midlatitudes. We explore the representation and prediction skill for the KE in the GFDL SPEAR (Seamless System for Prediction and Earth System Research) coupled model. Two different approaches are used to generate coupled reanalyses and forecasts: 1) restoring the coupled model’s SST and atmospheric variables toward existing reanalyses, or 2) assimilating SST and subsurface observations into the coupled model without atmospheric assimilation. Both systems use an ocean model with 1° resolution and capture the largest sea surface height (SSH) variability over the KE region. Assimilating subsurface observations appears to be essential to reproduce the narrow front and related oceanic variability of the KE jet in the coupled reanalysis. We demonstrate skillful retrospective predictions of KE SSH variability in monthly (up to 1 year) and annual-mean (up to 5 years) KE forecasts in the seasonal and decadal prediction systems, respectively. The prediction skill varies seasonally, peaking for forecasts initialized in January and verifying in September due to the winter intensification of North Pacific atmospheric forcing. We show that strong large-scale atmospheric anomalies generate deterministic oceanic forcing (i.e., Rossby waves), leading to skillful long-lead KE forecasts. These atmospheric anomalies also drive Ekman convergence and divergence, which forms ocean memory, by sequestering thermal anomalies deep into the winter mixed layer that re-emerge in the subsequent autumn. The SPEAR forecasts capture the recent negative-to-positive transition of the KE phase in 2017, projecting a continued positive phase through 2022.
The frequency of large-scale anomalous precipitation events associated with heavy precipitation has been increasing in Japan. However, it is unclear if the increase is due to anthropogenic warming or internal variability. Also, it is challenging to develop an objective methodology to identify anomalous events because of the large variety of anomalous precipitation cases. In this study, we applied a deep learning technique to objectively detect anomalous precipitation events in Japan for both observations and simulations using high-resolution climate models. The results show that the observed increases in anomalous heavy precipitation events in Western Japan during 1977–2015 were not made only by internal variability but the increases in anthropogenic forcing played an important role. Such events will continue to increase in frequency this century. The increases are attributable to the increasing frequency of tropical cyclones and enhanced frontal rainbands near Japan. These results highlight the mitigation challenge posed by the increasing occurrence of unprecedented precipitation events in the future.
Quantifying the response of atmospheric rivers (ARs) to radiative forcing is challenging due to uncertainties caused by internal climate variability, differences in shared socioeconomic pathways (SSPs), and methods used in AR detection algorithms. In addition, the requirement of medium-to-high model resolution and ensemble sizes to explicitly simulate ARs and their statistics can be computationally expensive. In this study, we leverage the unique 50-km large ensembles generated by a Geophysical Fluid Dynamics Laboratory next-generation global climate model, Seamless system for Prediction and EArth system Research, to explore the warming response in ARs. Under both moderate and high emissions scenarios, increases in AR-day frequency emerge from the noise of internal variability by 2060. This signal is robust across different SSPs and time-independent detection criteria. We further examine an alternative approach proposed by Thompson et al. (2015), showing that unforced AR variability can be approximated by a first-order autoregressive process. The confidence intervals of the projected response can be analytically derived with a single ensemble member.
A subseasonal-to-seasonal (S2S) prediction system was recently developed using the GFDL Seamless System for Prediction and Earth System Research (SPEAR) global coupled model. Based on 20-yr hindcast results (2000–19), the boreal wintertime (November–April) Madden–Julian oscillation (MJO) prediction skill is revealed to reach 30 days measured before the anomaly correlation coefficient of the real-time multivariate (RMM) index drops to 0.5. However, when the MJO is partitioned into four distinct propagation patterns, the prediction range extends to 38, 31, and 31 days for the fast-propagating, slow-propagating, and jumping MJO patterns, respectively, but falls to 23 days for the standing MJO. A further improvement of MJO prediction requires attention to the standing MJO given its large gap with its potential predictability (38 days). The slow-propagating MJO detours southward when traversing the Maritime Continent (MC), and confronts the MC prediction barrier in the model, while the fast-propagating MJO moves across the central MC without this prediction barrier. The MJO diversity is modulated by stratospheric quasi-biennial oscillation (QBO): the standing (slow-propagating) MJO coincides with significant westerly (easterly) phases of QBO, partially explaining the contrasting MJO prediction skill between these two QBO phases. The SPEAR model shows its capability, beyond the propagation, in predicting their initiation for different types of MJO along with discrete precursory convection anomalies. The SPEAR model skillfully predicts the observed distinct teleconnections over the North Pacific and North America related to the standing, jumping, and fast-propagating MJO, but not the slow-propagating MJO. These findings highlight the complexities and challenges of incorporating MJO prediction into the operational prediction of meteorological variables.
One of the most puzzling observed features of recent climate has been a multidecadal surface cooling trend over the subpolar Southern Ocean (SO). In this study we use large ensembles of simulations with multiple climate models to study the role of the SO meridional overturning circulation (MOC) in these sea surface temperature (SST) trends. We find that multiple competing processes play prominent roles, consistent with multiple mechanisms proposed in the literature for the observed cooling. Early in the simulations (twentieth century and early twenty-first century) internal variability of the MOC can have a large impact, in part due to substantial simulated multidecadal variability of the MOC. Ensemble members with initially strong convection (and related surface warming due to convective mixing of subsurface warmth to the surface) tend to subsequently cool at the surface as convection associated with internal variability weakens. A second process occurs in the late-twentieth and twenty-first centuries, as weakening of oceanic convection associated with global warming and high-latitude freshening can contribute to the surface cooling trend by suppressing convection and associated vertical mixing of subsurface heat. As the simulations progress, the multidecadal SO variability is suppressed due to forced changes in the mean state and increased oceanic stratification. As a third process, the shallower mixed layers can then rapidly warm due to increasing forcing from greenhouse gas warming. Also, during this period the ensemble spread of SO SST trend partly arises from the spread of the wind-driven Deacon cell strength. Thus, different processes could conceivably have led to the observed cooling trend, consistent with the range of possibilities presented in the literature. To better understand the causes of the observed trend, it is important to better understand the characteristics of internal low-frequency variability in the SO and the response of that variability to global warming.
Compared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarctic sea ice. These systems, based on the FLOR, SPEAR_LO, and SPEAR_MED dynamical models, differ in their coupled model components, initialization techniques, atmospheric resolution, and model biases. Using suites of retrospective initialized seasonal predictions spanning 1992–2018, we investigate the role of these factors in determining Antarctic sea ice prediction skill and examine the mechanisms of regional sea ice predictability. We find that each system is capable of skillfully predicting regional Antarctic sea ice extent (SIE) with skill that exceeds a persistence forecast. Winter SIE is skillfully predicted 11 months in advance in the Weddell, Amundsen/Bellingshausen, Indian, and west Pacific sectors, whereas winter skill is notably lower in the Ross sector. Zonally advected upper-ocean heat content anomalies are found to provide the crucial source of prediction skill for the winter sea ice edge position. The recently developed SPEAR systems are more skillful than FLOR for summer sea ice predictions, owing to improvements in sea ice concentration and sea ice thickness initialization. Summer Weddell SIE is skillfully predicted up to 9 months in advance in SPEAR_MED, due to the persistence and drift of initialized sea ice thickness anomalies from the previous winter. Overall, these results suggest a promising potential for providing operational Antarctic sea ice predictions on seasonal time scales.
The recent multi-year 2015–2019 drought after a multi-decadal drying trend over Central America raises the question of whether anthropogenic climate change (ACC) played a role in exacerbating these events. While the occurrence of the 2015–2019 drought in Central America has been asserted to be associated with ACC, we lack an assessment of natural vs anthropogenic contributions. Here, we use five different large ensembles—including high-resolution ensembles (i.e., 0.5∘ horizontally)—to estimate the contribution of ACC to the probability of occurrence of the 2015–2019 event and the recent multi-decadal trend. The comparison of ensembles forced with natural and natural plus anthropogenic forcing suggests that the recent 40-year trend is likely associated with internal climate variability. However, the 2015–2019 rainfall deficit has been made more likely by ACC. The synthesis of the results from model ensembles supports the notion of a significant increase, by a factor of four, over the last century for the 2015–2019 meteorological drought to occur because of ACC. All the model results further suggest that, under intermediate and high emission scenarios, the likelihood of similar drought events will continue to increase substantially over the next decades.
Tseng, Kai-Chih, Nathaniel C Johnson, Eric Maloney, Elizabeth A Barnes, and Sarah B Kapnick, June 2021: Mapping large-scale climate variability to hydrological extremes: An application of the linear inverse model to subseasonal prediction. Journal of Climate, 34(11), DOI:10.1175/JCLI-D-20-0502.1. Abstract
The excitation of the Pacific–North American (PNA) teleconnection pattern by the Madden–Julian oscillation (MJO) has been considered one of the most important predictability sources on subseasonal time scales over the extratropical Pacific and North America. However, until recently, the interactions between tropical heating and other extratropical modes and their relationships to subseasonal prediction have received comparatively little attention. In this study, a linear inverse model (LIM) is applied to examine the tropical–extratropical interactions. The LIM provides a means of calculating the response of a dynamical system to a small forcing by constructing a linear operator from the observed covariability statistics of the system. Given the linear assumptions, it is shown that the PNA is one of a few leading modes over the extratropical Pacific that can be strongly driven by tropical convection while other extratropical modes present at most a weak interaction with tropical convection. In the second part of this study, a two-step linear regression is introduced that leverages a LIM and large-scale climate variability to the prediction of hydrological extremes (e.g., atmospheric rivers) on subseasonal time scales. Consistent with the findings of the first part, most of the predictable signals on subseasonal time scales are determined by the dynamics of the MJO–PNA teleconnection while other extratropical modes are important only at the shortest forecast leads.
Atmospheric rivers (ARs) exert significant socioeconomic impacts in western North America, where 30% of the annual precipitation is determined by ARs that occur in less than 15% of wintertime. ARs are thus beneficial to water supply but can produce extreme precipitation hazards when making landfall. While most prevailing research has focused on the subseasonal (<5 weeks) prediction of ARs, only limited efforts have been made for AR forecasts on multiseasonal timescales (>3 months) that are crucial for water resource management and disaster preparedness. Through the analysis of reanalysis data and retrospective predictions from a new seasonal-to-decadal forecast system, this research shows the existing potential of multiseasonal AR frequency forecasts with predictive skills 9 months in advance. Additional analysis explores the dominant predictability sources and challenges for multiseasonal AR prediction.
An extended period of high temperatures across the state of Alaska in June and July, 2019 set multiple temperature records. Here, we examine the extent to which human-driven climate change played a role in increasing the likelihood of experiencing such an extreme event. Using global climate models, we determine that human-driven climate change increased the probability of experiencing such high temperatures in 2019, and that the likelihood of similar or more extreme events will increase into the coming century.
Midlatitude baroclinic waves drive extratropical weather and climate variations, but their predictability beyond 2 weeks has been deemed low. Here we analyze a large ensemble of climate simulations forced by observed sea surface temperatures (SSTs) and demonstrate that seasonal variations of baroclinic wave activity (BWA) are potentially predictable. This potential seasonal predictability is denoted by robust BWA responses to SST forcings. To probe regional sources of the potential predictability, a regression analysis is applied to the SST-forced large ensemble simulations. By filtering out variability internal to the atmosphere and land, this analysis identifies both well-known and unfamiliar BWA responses to SST forcings across latitudes. Finally, we confirm the model-indicated predictability by showing that an operational seasonal prediction system can leverage some of the identified SST-BWA relationships to achieve skillful predictions of BWA. Our findings help to extend long-range predictions of the statistics of extratropical weather events and their impacts.
Barcikowska, Monika, Sarah B Kapnick, and Lakshmi Krishnamurthy, et al., February 2020: Changes in the future summer Mediterranean climate: contribution of teleconnections and local factors. Earth System Dynamics, 11(1), DOI:10.5194/esd-11-161-2020. Abstract
This study analyzes future climate for the Mediterranean region projected with the high-resolution coupled CM2.5 model, which incorporates a new and improved land model (LM3). The simulated climate changes suggest pronounced warming and drying over most of the region. However, the changes are distinctly smaller than those of the CMIP5 multi-model ensemble. In addition, the changes over much of southeast and central Europe indicate very modest warming compared to the CMIP5 projections and also a tendency toward wetter conditions. These differences indicate a possible role of factors such as land surface–atmospheric interactions in these regions. Our analysis also highlights the importance of correctly projecting the magnitude of changes in the summer North Atlantic Oscillation, which has the capacity to partly offset anthropogenic warming and drying over the western and central Mediterranean. Nevertheless, the projections suggest a decreasing influence of local atmospheric dynamics and teleconnections in maintaining the regional temperature and precipitation balance, in particular over arid regions like the eastern and southern Mediterranean, which show a local maximum of warming and drying. The intensification of the heat low in these regions rather suggests an increasing influence of warming land surface on the local surface atmospheric circulation and progressing desertification.
We document the development and simulation characteristics of the next generation modeling system for seasonal to decadal prediction and projection at the Geophysical Fluid Dynamics Laboratory (GFDL). SPEAR (Seamless System for Prediction and EArth System Research) is built from component models recently developed at GFDL ‐ the AM4 atmosphere model, MOM6 ocean code, LM4 land model and SIS2 sea ice model. The SPEAR models are specifically designed with attributes needed for a prediction model for seasonal to decadal time scales, including the ability to run large ensembles of simulations with available computational resources. For computational speed SPEAR uses a coarse ocean resolution of approximately 1.0o (with tropical refinement). SPEAR can use differing atmospheric horizontal resolutions ranging from 1o to 0.25o. The higher atmospheric resolution facilitates improved simulation of regional climate and extremes. SPEAR is built from the same components as the GFDL CM4 and ESM 4 models, but with design choices geared toward seasonal to multidecadal physical climate prediction and projection. We document simulation characteristics for the time‐mean climate, aspects of internal variability, and the response to both idealized and realistic radiative forcing change. We describe in greater detail one focus of the model development process that was motivated by the importance of the Southern Ocean to the global climate system. We present sensitivity tests that document the influence of the Antarctic surface heat budget on Southern Ocean ventilation and deep global ocean circulation. These findings were also useful in the development processes for the GFDL CM4 and ESM 4 models.
Hurwitz, Margaret M., Stephen Baxter, Bonnie Brown, Jessie Carman, Jordan Dale, Clara Draper, Fiona Horsfall, Mimi Hughes, Jordan Gerth, and Sarah B Kapnick, et al., November 2020: Six priorities for investment in snow research and product development. Bulletin of the American Meteorological Society, 101(11), DOI:10.1175/BAMS-D-20-0218.1E2025-E2029.
Positive precipitation biases over western North America have remained a pervasive problem in the current generation of coupled global climate models. These biases are substantially reduced, however, in a version of the Geophysical Fluid Dynamics Laboratory Forecast-oriented Low Ocean Resolution (FLOR) coupled climate model with systematic sea surface temperature (SST) biases artificially corrected through flux adjustment. This study examines how the SST biases in the Atlantic and Pacific Oceans contribute to the North American precipitation biases. Experiments with the FLOR model in which SST biases are removed in the Atlantic and Pacific are carried out to determine the contribution of SST errors in each basin to precipitation statistics over North America. Tropical and North Pacific SST biases have a strong impact on northern North American precipitation, while tropical Atlantic SST biases have a dominant impact on precipitation biases in southern North America, including the western United States. Most notably, negative SST biases in the tropical Atlantic in boreal winter induce an anomalously strong Aleutian low and a southward bias in the North Pacific storm track. In boreal summer, the negative SST biases induce a strengthened North Atlantic Subtropical High and Great Plains low-level jet. Each of these impacts contributes to positive annual mean precipitation biases over western North America. Both North Pacific and North Atlantic SST biases induce SST biases in remote basins through dynamical pathways, so a complete attribution of the effects of SST biases on precipitation must account for both the local and remote impacts.
High Mountain Asia (HMA) is impacted by extreme monsoonal rainfall that triggers landslides in large proportions relative to global distributions, resulting in substantial human impacts and damage to infrastructure each year. Previous landslide research has qualitatively estimated how patterns in landslide activity may change based on climate change scenarios. We present the first quantitative view of potential modulation in future landslide activity over the HMA region leveraging a new landslide hazard model and precipitation data from satellite and Global Climate Model (GCM) sources. In doing so, we find that the rate of increase in landslide activity at the end of the century is expected to be greatest over areas covered by current glaciers and glacial lakes, potentially exacerbating the impacts of cascading hazards on populations downstream. This work demonstrates the potential of GCMs and satellite‐based precipitation estimates to characterize landslide hazards at timescales affected by climate change.
Three consecutive dry winters (2015–2017) in southwestern South Africa (SSA) resulted in the Cape Town “Day Zero” drought in early 2018. The contribution of anthropogenic global warming to this prolonged rainfall deficit has previously been evaluated through observations and climate models. However, model adequacy and insufficient horizontal resolution make it difficult to precisely quantify the changing likelihood of extreme droughts, given the small regional scale. Here, we use a high-resolution large ensemble to estimate the contribution of anthropogenic climate change to the probability of occurrence of multiyear SSA rainfall deficits in past and future decades. We find that anthropogenic climate change increased the likelihood of the 2015–2017 rainfall deficit by a factor of five to six. The probability of such an event will increase from 0.7 to 25% by the year 2100 under an intermediate-emission scenario (Shared Socioeconomic Pathway 2-4.5 [SSP2-4.5]) and to 80% under a high-emission scenario (SSP5-8.5). These results highlight the strong sensitivity of the drought risk in SSA to future anthropogenic emissions.
Catalano, A J., Anthony J Broccoli, Sarah B Kapnick, and Tyler P Janoski, April 2019: High-Impact Extratropical Cyclones along the Northeast Coast of the United States in a Long Coupled Climate Model Simulation. Journal of Climate, 32(7), DOI:10.1175/JCLI-D-18-0376.1. Abstract
High-impact extratropical cyclones (ETCs) cause considerable damage along the Northeast coast of the United States through strong winds and inundation, but these relatively rare events are difficult to analyze owing to limited historical records. Using a 1505-year simulation from the GFDL FLOR coupled model, statistical analyses of extreme events are performed including exceedance probability computations to compare estimates from shorter segments to estimates that could be obtained from a record of considerable length. The most extreme events possess characteristics including exceptionally low central pressure, hurricane-force winds, and a large surge potential, which would greatly impact nearby regions. Return level estimates of metrics of ETC intensity using shorter, historical-length segments of the FLOR simulation are underestimated compared to levels determined using the full simulation. This indicates that if the underlying distributions of observed ETC metrics are similar to those of the 1505-year FLOR distributions, the actual frequency of extreme ETC events could also be underestimated.
Comparisons between FLOR and reanalysis products suggest that not all features of simulated high-impact ETCs are representative of observations. Spatial track densities are similar, but FLOR exhibits a negative bias in central pressure and a positive bias in wind speed, particularly for more intense events. Although the existence of these model biases precludes the quantitative use of model-derived return statistics as a substitute for those derived from shorter observational records, this work suggests that statistics from future models of higher fidelity could be used to better constrain the probability of extreme ETC events and their impacts.
Lundquist, J K., Mimi Hughes, E Gutmann, and Sarah B Kapnick, December 2019: Our skill in modeling mountain rain and snow is bypassing the skill of our observational networks. Bulletin of the American Meteorological Society, 100(12), DOI:10.1175/BAMS-D-19-0001.1. Abstract
We have now crossed a threshold where, for many mountain ranges, well-configured highresolution atmospheric models are better able to represent range-wide total annual precipitation than the collective network of precipitation gauges, i.e., observations.
In mountain terrain, well-configured high-resolution atmospheric models are able to simulate total annual rain and snowfall better than spatial estimates derived from in situ observational networks of precipitation gauges, and significantly better than radar or satellite-derived estimates. This conclusion is primarily based on comparisons with streamflow and snow in basins across the Western United States and in Iceland, Europe, and Asia. Even though they outperform gridded datasets based on gauge-networks, atmospheric models still disagree with each other on annual average precipitation and often disagree more on their representation of individual storms. Research to address these difficulties must make use of a wide range of observations (snow, streamflow, ecology, radar, satellite) and bring together scientists from different disciplines and a widerange of communities.
The Angola Low is a summertime low-pressure system that affects the convergence of low-level moisture fluxes into southern Africa. Interannual variations of the Angola Low reduce the seasonal prediction skills for this region that arise from coupled atmosphere-ocean variability. Despite its importance, the interannual dynamics of the Angola Low, and its relationship with El Niño-Southern Oscillation (ENSO) and other coupled modes of variability, are still poorly understood, mostly because of the scarcity of atmospheric data and short-term duration of atmospheric reanalyses in the region. To bypass this issue, we use a long-term (3500 years) run from a 50-km-resolution global coupled model capable of simulating the summertime southern African large-scale circulation and teleconnections. We find that the meridional displacement and strength of the Angola Low are moderately modulated by local sea surface temperature anomalies, especially those in proximity of the southeastern African coast, and to a lesser extent by ENSO and other coupled atmosphere-ocean modes like the Subtropical Indian Ocean Dipole. Comparison of the coupled run with a 1000-year run driven by climatological sea surface temperatures reveals that the interannual excursions of the Angola Low are in both cases associated with geopotential height anomalies over the southern Atlantic and Indian Ocean related to extratropical atmospheric variability. Midlatitude atmospheric variability explains almost 60% of the variance of the Angola Low variability in the uncoupled run, but only 20% in the coupled run. Therefore, while the Angola Low appears to be intrinsically controlled by atmospheric extratropical variability, the interference of the atmospheric response forced by tropical sea surface temperature anomalies weakens this influence.
Most dust forecast models focus on a short, sub‐seasonal lead times, i.e., three to six days, and the skill of seasonal prediction is not clear. In this study we examine the potential of seasonal dust prediction in the U.S. using an observation‐constrained regression model and key variables predicted by a seasonal prediction model developed at NOAA Geophysical Fluid Dynamics Laboratory, the Forecast‐Oriented Low Ocean Resolution (FLOR) Model.
Our method shows skillful predictions of spring dustiness three to six months in advance. It is found that the regression model explains about 71% of the variances of dust event frequency over the Great Plains and 63% over the southwestern U.S. in March‐May from 2004 to 2016 using predictors from FLOR initialized on December 1st. Variations in springtime dustiness are dominated by springtime climatic factors rather than wintertime factors. Findings here will help development of a seasonal dust prediction system and hazard prevention.
The 2018 tropical cyclone (TC) season in the North Pacific was very active, with 39 tropical storms including 8 typhoons/hurricanes. This activity was successfully predicted up to 5 months in advance by the Geophysical Fluid Dynamics Laboratory Forecast‐oriented Low Ocean Resolution (FLOR) global coupled model. In this work, a suite of idealized experiments with three dynamical global models (FLOR, NICAM and MRI‐AGCM) was used to show that the active 2018 TC season was primarily caused by warming in the subtropical Pacific, and secondarily by warming in the tropical Pacific. Furthermore, the potential effect of anthropogenic forcing on the active 2018 TC season was investigated using two of the global models (FLOR and MRI‐AGCM). The models projected opposite signs for the changes in TC frequency in the North Pacific by an increase in anthropogenic forcing, thereby highlighting the substantial uncertainty and model dependence in the possible impact of anthropogenic forcing on Pacific TC activity.
Responses of tropical cyclones (TCs) to CO2 doubling are explored using coupled global climate models (GCMs) with increasingly refined atmospheric/land horizontal grids (~ 200 km, ~ 50 km and ~ 25 km). The three models exhibit similar changes in background climate fields thought to regulate TC activity, such as relative sea surface temperature (SST), potential intensity, and wind shear. However, global TC frequency decreases substantially in the 50 km model, while the 25 km model shows no significant change. The ~ 25 km model also has a substantial and spatially-ubiquitous increase of Category 3–4–5 hurricanes. Idealized perturbation experiments are performed to understand the TC response. Each model’s transient fully-coupled 2 × CO2 TC activity response is largely recovered by “time-slice” experiments using time-invariant SST perturbations added to each model’s own SST climatology. The TC response to SST forcing depends on each model’s background climatological SST biases: removing these biases leads to a global TC intensity increase in the ~ 50 km model, and a global TC frequency increase in the ~ 25 km model, in response to CO2-induced warming patterns and CO2 doubling. Isolated CO2 doubling leads to a significant TC frequency decrease, while isolated uniform SST warming leads to a significant global TC frequency increase; the ~ 25 km model has a greater tendency for frequency increase. Global TC frequency responds to both (1) changes in TC “seeds”, which increase due to warming (more so in the ~ 25 km model) and decrease due to higher CO2 concentrations, and (2) less efficient development of these“seeds” into TCs, largely due to the nonlinear relation between temperature and saturation specific humidity.
Barcikowska, Monika, Sarah B Kapnick, and F Feser, March 2018: Impact of large-scale circulation changes in the North Atlantic sector on the current and future Mediterranean winter hydroclimate. Climate Dynamics, 50(5-6), DOI:10.1007/s00382-017-3735-5. Abstract
The Mediterranean region, located in the transition zone between the dry subtropical and wet European mid-latitude climate, is very sensitive to changes in the global mean climate state. Projecting future changes of the Mediterranean hydroclimate under global warming therefore requires dynamic climate models to reproduce the main mechanisms controlling regional hydroclimate with sufficiently high resolution to realistically simulate climate extremes. To assess future winter precipitation changes in the Mediterranean region we use the Geophysical Fluid Dynamics Laboratory high-resolution general circulation model for control simulations with pre-industrial greenhouse gas and aerosol concentrations which are compared to future scenario simulations. Here we show that the coupled model is able to reliably simulate the large-scale winter circulation, including the North Atlantic Oscillation and Eastern Atlantic patterns of variability, and its associated impacts on the mean Mediterranean hydroclimate. The model also realistically reproduces the regional features of daily heavy rainfall, which are absent in lower-resolution simulations. A five-member future projection ensemble, which assumes comparatively high greenhouse gas emissions (RCP8.5) until 2100, indicates a strong winter decline in Mediterranean precipitation for the coming decades. Consistent with dynamical and thermodynamical consequences of a warming atmosphere, derived changes feature a distinct bipolar behavior, i.e. wetting in the north—and drying in the south. Changes are most pronounced over the northwest African coast, where the projected winter precipitation decline reaches 40% of present values. Despite a decrease in mean precipitation, heavy rainfall indices show drastic increases across most of the Mediterranean, except the North African coast, which is under the strong influence of the cold Canary Current.
Eastern North America contains densely populated, highly developed areas, making winter storms with strong winds and high snowfall among the costliest storm types. For this reason, it is important to determine how the frequency of high-impact winter storms, specifically those combining significant snowfall and winds, will change in this region under increasing greenhouse gas concentrations. This study uses a high-resolution coupled global climate model to simulate the changes in extreme winter conditions from the present climate to a future scenario with doubled-CO2 concentrations (2XC). In particular, this study focuses on changes in high snowfall, extreme wind (HSEW) events, which are defined as the occurrence of two-day snowfall and high winds exceeding thresholds based on extreme values from the control simulation where greenhouse gas concentrations remain fixed. Mean snowfall consistently decreases across the entire region, but extreme snowfall shows a more inconsistent pattern with some areas experiencing increases in the frequency of extreme snowfall events. Extreme wind events show relatively small changes in frequency with 2XC, with the exception of high-elevation areas where there are large decreases in frequency. As a result of combined changes in wind and snowfall, HSEW events decrease in frequency in the 2XC simulation for much of the eastern North America. Changes in the number of HSEW events in the 2XC environment are driven mainly by changes in the frequency of extreme snowfall events, with most of the region experiencing decreases in event frequency, except for certain inland areas at higher latitudes.
Mountain snowpack in the western United States provides a natural reservoir for cold season precipitation; variations in snowpack influence warm season water supply, wildfire risk, ecology, and industries like agriculture dependent on snow and downstream water availability. Efforts to understand snowpack variability have predominantly been focused on either weekly (weather) or decadal to centennial (climate variability and change) timescales. We focus on a timescale between these ranges by demonstrating that a global climate model suite can provide snowpack predictions 8 months in advance. The predictions from climate models outperform statistical methods from observations alone. Our results show that seasonal hydroclimate predictions are possible and highlight areas for future prediction system improvements.
Unprecedented high intensity flooding induced by extreme precipitation was reported over Chennai in India during November-December of 2015, which led to extensive damage to human life and property. It is of utmost importance to determine the odds of occurrence of such extreme floods in future and the related climate phenomena, for planning and mitigation purposes. Here, we make use of a suite of simulations from GFDL high-resolution coupled climate models to investigate the odds of occurrence of extreme floods induced by extreme precipitation over Chennai and the role of radiative forcing and/or large-scale SST forcing in enhancing the probability of such events in future. Climate of 20th century experiments with large ensembles suggest that the radiative forcing may not enhance the probability of extreme floods over Chennai. Doubling of CO2 experiments also fail to show evidence for increase of such events in a global warming scenario. Further, this study explores the role of SST forcing from the Indian and Pacific Oceans on the odds of occurrence of Chennai-like floods. Neither an El Niño nor La Niña enhances the probability of extreme floods over Chennai. However, warm Bay of Bengal tends to increase the odds of occurrence of extreme Chennai-like floods. The atmospheric condition such as a tropical depression over Bay of Bengal favoring the transport of moisture from warm Bay of Bengal is conducive for intense precipitation.
Widespread multiday convective bursts in the southwestern United States during the North American monsoon are often triggered by Gulf of California moisture surges (GoC surges). However, how GoC surges, and the amount and intensity of associated precipitation, will change in response to CO2-induced warming remains little known, not least because the most widely available climate models do not currently resolve the relevant mesoscale dynamics due to their coarse resolution (100 km or more). In this study, a 50-km resolution global coupled model (FLOR) is used to address this question. It is found that the mean number of GoC surge events remains unchanged under CO2 doubling, but intermediate-to-high intensity surge-related precipitation tends to become less frequent, thus reducing the mean summertime rainfall. Lowlevel moisture fluxes associated with GoC surges as well as their convergence over land to the east of the GoC intensify, but the increases in low-level moisture are not matched by the larger increments in the near-surface saturation specific humidity due to amplified land warming. This results in a more unsaturated, low-level atmospheric environment which disfavors moist convection. These thermodynamic changes are accompanied by dynamics changes that are also less conducive to convective activity, with the mid-level monsoonal ridge projected to expand and move to the west of its present-day climatological maximum. Despite the overall reduction in precipitation, the frequency of very intense, localized daily surge-related precipitation in Arizona and surrounding areas is projected to increase, consistently with increased precipitable water.
Floods in the Mississippi basin can have large negative societal, natural and economic impacts. Understanding the drivers of floods, now and in the future, is relevant for risk management and infrastructure-planning purposes. We investigate the drivers of 100-year return Lower-Mississippi River floods using a global coupled climate model with an integrated surface-water module. The model provides 3400 years of physically consistent data from a static climate, in contrast to available observational data (relatively short records, incomplete land-surface data, transient climate). In the months preceding the model’s 100-year floods, as indicated by extreme monthly discharge, above-average rain and snowfall lead to moist subsurface conditions and the build up of snowpack, making the river system prone to these major flooding events. The melt water from snowpack in the northern Missouri and Upper Mississippi catchments primes the river system, sensitizing it to subsequent above-average precipitation in the Ohio and Tennessee catchments. An ensemble of transient-forcing experiments is used to investigate the impacts of past and projected anthropogenic climate change on extreme floods. There is no statistically significant projected trend in the occurrence of 100-year floods in the model ensemble, despite significant increases in extreme precipitation, significant decreases in extreme snowmelt, and significant decreases in less extreme floods. The results emphasize the importance of considering the fully-coupled land-atmosphere system for extreme floods. This initial analysis provides avenues for further investigation, including comparison to characteristics of less extreme floods, the sensitivity to model configuration, the role of human water management, and implications for future flood-risk management.
Wrzesien, M L., M T Durand, T M Pavelsky, and Sarah B Kapnick, et al., February 2018: A new estimate of North American mountain snow accumulation from regional climate model simulations. Geophysical Research Letters, 45(3), DOI:10.1002/2017GL076664. Abstract
Despite the importance of mountain snowpack to understanding the water and energy cycles in North America's montane regions, no reliable mountain snow climatology exists for the entire continent. We present a new estimate of mountain snow water equivalent (SWE) for North America from regional climate model simulations. Climatological peak SWE in North America mountains is 1006 km3, 2.94 times larger than previous estimates from reanalyses. By combining this mountain SWE value with the best available global product in non-mountain areas, we estimate peak North America SWE of 1684 km3, 55% greater than previous estimates. In our simulations, the date of maximum SWE varies widely by mountain range, from early March to mid-April. Though mountains comprise 24% of the continent's land area, we estimate that they contain ~60% of North American SWE. This new estimate is a suitable benchmark for continental- and global-scale water and energy budget studies.
A “typical” El Niño leads to wet (dry) wintertime anomalies over the southern (northern) half of the Western United States (WUS). However, during the strong El Niño of 2015/16, the WUS winter precipitation pattern was roughly opposite to this canonical (average of the record) anomaly pattern. To understand why this happened, and whether it was predictable, we use a suite of high-resolution seasonal prediction experiments with coupled climate models. We find that the unusual 2015/16 precipitation pattern was predictable at zero-lead time horizon when the ocean/atmosphere/land components were initialized with observations. However, when the ocean alone is initialized the coupled model fails to predict the 2015/16 pattern, although ocean initial conditions alone can reproduce the observed WUS precipitation during the 1997/98 strong El Niño. Further observational analysis shows that the amplitudes of the El Niño induced tropical circulation anomalies during 2015/16 were weakened by about 50% relative to those of 1997/98. This was caused by relative cold (warm) anomalies in the eastern (western) tropical Pacific suppressing (enhancing) deep convection anomalies in the eastern (western) tropical Pacific during 2015/16. The reduced El Niño teleconnection led to a weakening of the subtropical westerly jet over the southeast North Pacific and southern WUS, resulting in the unusual 2015/16 winter precipitation pattern over the WUS. This study highlights the importance of initial conditions not only in the ocean, but in the land and atmosphere as well, for predicting the unusual El Niño teleconnection and its influence on the winter WUS precipitation anomalies during 2015/16.
Future changes in the North American monsoon, a circulation system that brings abundant summer rains to vast areas of the North American Southwest1, 2, could have significant consequences for regional water resources3. How this monsoon will change with increasing greenhouse gases, however, remains unclear4, 5, 6, not least because coarse horizontal resolution and systematic sea-surface temperature biases limit the reliability of its numerical model simulations5, 7. Here we investigate the monsoon response to increased atmospheric carbon dioxide (CO2) concentrations using a 50-km-resolution global climate model which features a realistic representation of the monsoon climatology and its synoptic-scale variability8. It is found that the monsoon response to CO2 doubling is sensitive to sea-surface temperature biases. When minimizing these biases, the model projects a robust reduction in monsoonal precipitation over the southwestern United States, contrasting with previous multi-model assessments4, 9. Most of this precipitation decline can be attributed to increased atmospheric stability, and hence weakened convection, caused by uniform sea-surface warming. These results suggest improved adaptation measures, particularly water resource planning, will be required to cope with projected reductions in monsoon rainfall in the American Southwest.
Tommasi, Desiree, Charles A Stock, A J Hobday, R Methot, Isaac C Kaplan, J P Eveson, Kirstin Holsman, Timothy J Miller, Sarah K Gaichas, Marion Gehlen, A Pershing, Gabriel A Vecchi, Rym Msadek, Thomas L Delworth, C M Eakin, Melissa A Haltuch, Roland Séférian, C M Spillman, J R Hartog, Samantha A Siedlecki, Jameal F Samhouri, Barbara A Muhling, R G Asch, Malin L Pinsky, Vincent S Saba, Sarah B Kapnick, and Carlos F Gaitán, et al., March 2017: Managing living marine resources in a dynamic environment: The role of seasonal to decadal climate forecasts. Progress in Oceanography, 152, DOI:10.1016/j.pocean.2016.12.011. Abstract
Recent developments in global dynamical climate prediction systems have allowed for skillful predictions of climate variables relevant to living marine resources (LMRs) at a scale useful to understanding and managing LMRs. Such predictions present opportunities for improved LMR management and industry operations, as well as new research avenues in fisheries science. LMRs respond to climate variability via changes in physiology and behavior. For species and systems where climate-fisheries links are well established, forecasted LMR responses can lead to anticipatory and more effective decisions, benefitting both managers and stakeholders. Here, we provide an overview of climate prediction systems and advances in seasonal to decadal prediction of marine-resource relevant environmental variables. We then describe a range of climate-sensitive LMR decisions that can be taken at lead-times of months to decades, before highlighting a range of pioneering case studies using climate predictions to inform LMR decisions. The success of these case studies suggests that many additional applications are possible. Progress, however, is limited by observational and modeling challenges. Priority developments include strengthening of the mechanistic linkages between climate and marine resource responses, development of LMR models able to explicitly represent such responses, integration of climate driven LMR dynamics in the multi-driver context within which marine resources exist, and improved prediction of ecosystem-relevant variables at the fine regional scales at which most marine resource decisions are made. While there are fundamental limits to predictability, continued advances in these areas have considerable potential to make LMR managers and industry decision more resilient to climate variability and help sustain valuable resources. Concerted dialog between scientists, LMR managers and industry is essential to realizing this potential.
van der Wiel, Karin, Sarah B Kapnick, G J van Oldenborgh, K Whan, S Philip, Gabriel A Vecchi, R K Singh, J Arrighi, and H Cullen, February 2017: Rapid attribution of the August 2016 flood-inducing extreme precipitation in south Louisiana to climate change. Hydrology and Earth System Sciences, 21(2), DOI:10.5194/hess-21-897-2017. Abstract
A stationary low pressure system and elevated levels of precipitable water provided a nearly continuous source of precipitation over Louisiana, United States (U.S.) starting around 10 August, 2016. Precipitation was heaviest in the region broadly encompassing the city of Baton Rouge, with a three-day maximum found at a station in Livingston, LA (east of Baton Rouge) from 12–14 August, 2016 (648.3 mm, 25.5 inches). The intense precipitation was followed by inland flash flooding and river flooding and in subsequent days produced additional backwater flooding. On 16 August, Louisiana officials reported that 30,000 people had been rescued, nearly 10,600 people had slept in shelters on the night of 14 August, and at least 60,600 homes had been impacted to varying degrees. As of 17 August, the floods were reported to have killed at least thirteen people. As the disaster was unfolding, the Red Cross called the flooding the worst natural disaster in the U.S. since Super Storm Sandy made landfall in New Jersey on 24 October, 2012. Before the floodwaters had receded, the media began questioning whether this extreme event was caused by anthropogenic climate change. To provide the necessary analysis to understand the potential role of anthropogenic climate change, a rapid attribution analysis was launched in real-time using the best readily available observational data and high-resolution global climate model simulations.
The objective of this study is to show the possibility of performing rapid attribution studies when both observational and model data, and analysis methods are readily available upon the start. It is the authors aspiration that the results be used to guide further studies of the devastating precipitation and flooding event. Here we present a first estimate of how anthropogenic climate change has affected the likelihood of a comparable extreme precipitation event in the Central U.S. Gulf Coast. While the flooding event of interest triggering this study occurred in south Louisiana, for the purposes of our analysis, we have defined an extreme precipitation event by taking the spatial maximum of annual 3-day inland maximum precipitation over the region: 29–31º N, 85–95º W, which we refer to as the Central U.S. Gulf Coast. Using observational data, we find that the observed local return time of the 12–14 August precipitation event in 2016 is about 550 years (95 % confidence interval (C.I.): 450–1450). The probability for an event like this to happen anywhere in the region is presently 1 in 30 years (C.I. 11–110). We estimate that these probabilities and the intensity of extreme precipitation events of this return time have increased since 1900. A Central U.S. Gulf Coast extreme precipitation event has effectively become more likely in 2016 than it was in 1900. The global climate models tell a similar story, with the regional probability of 3-day extreme precipitation increasing due to anthropogenic climate change by a factor of more than a factor 1.4 in the most accurate analyses. The magnitude of the shift in probabilities is greater in the 25 km (higher resolution) climate model than in the 50 km model. The evidence for a relation to El Niño half a year earlier is equivocal, with some analyses showing a positive connection and others none.
Climate change has been shown to impact the mean climate state and climate extremes. Though climate extremes have the potential to disrupt society, extreme conditions are rare by definition. In contrast, mild weather occurs frequently and many human activities are built around it. We provide a global analysis of mild weather based on simple criteria and explore changes in response to radiative forcing. We find a slight global mean decrease in the annual number of mild days projected both in the near future (−4 days per year, 2016–2035) and at the end of this century (−10 days per year, 2081–2100). Projected seasonal and regional redistributions of mild days are substantially greater. These changes are larger than the interannual variability of mild weather caused by El Niño–Southern Oscillation. Finally, we show an observed global decrease in the recent past, and that observed regional changes in mild weather resemble projections.
Lemoine, Derek, and Sarah B Kapnick, January 2016: A top-down approach to projecting market impacts of climate change. Nature Climate Change, 6(1), DOI:10.1038/nclimate2759. Abstract
To evaluate policies to reduce greenhouse-gas emissions, economic models require estimates of how future climate change will affect well-being. So far, nearly all estimates of the economic impacts of future warming have been developed by combining estimates of impacts in individual sectors of the economy1, 2. Recent work has used variation in warming over time and space to produce top-down estimates of how past climate and weather shocks have affected economic output3, 4, 5. Here we propose a statistical framework for converting these top-down estimates of past economic costs of regional warming into projections of the economic cost of future global warming. Combining the latest physical climate models, socioeconomic projections, and economic estimates of past impacts, we find that future warming could raise the expected rate of economic growth in richer countries, reduce the expected rate of economic growth in poorer countries, and increase the variability of growth by increasing the climate’s variability. This study suggests we should rethink the focus on global impacts and the use of deterministic frameworks for modelling impacts and policy.
The impact of atmosphere and ocean horizontal resolution on the climatology of North American Monsoon Gulf of California (GoC) moisture surges is examined in a suite of global circulation models (CM2.1, FLOR, CM2.5, CM2.6, HiFLOR) developed at the Geophysical Fluid Dynamics Laboratory (GFDL). These models feature essentially the same physical parameterizations, but differ in horizontal resolution in either the atmosphere (≃200, 50 and 25 km) or the ocean (≃1°, 0.25°, 0.1°). Increasing horizontal atmospheric resolution from 200 km to 50 km results in a drastic improvement in the model’s capability of accurately simulating surge events. The climatological near-surface flow and moisture and precipitation anomalies associated with GoC surges are overall satisfactorily simulated in all higher-resolution models. The number of surge events agrees well with reanalyses but models tend to underestimate July-August surge-related precipitation and overestimate September surge-related rainfall in the southwestern United States. Large-scale controls supporting the development of GoC surges, such as tropical easterly waves (TEWs), tropical cyclones (TCs) and trans-Pacific Rossby wave trains (RWTs), are also well captured, although models tend to underestimate the TEW/TC magnitude and number. Near-surface GoC surge features and their large-scale forcings (TEWs, TCs, RWTs) do not appear to be substantially affected by a finer representation of the GoC at higher ocean resolution. However, the substantial reduction of the eastern Pacific warm sea surface temperature bias through flux adjustment in the FLOR model leads to an overall improvement of tropical-extratropical controls on GoC moisture surges and the seasonal cycle of precipitation in the southwestern United States.
Precipitation extremes have a widespread impact on societies and ecosystems; it is therefore important to understand current and future patterns of extreme precipitation. Here, a set of new global coupled climate models with varying atmospheric resolution has been used to investigate the ability of these models to reproduce observed patterns of precipitation extremes and to investigate changes in these extremes in response to increased atmospheric CO2 concentrations. The atmospheric resolution was increased from 2°×2° grid cells (typical resolution in the CMIP5 archive) to 0.25°×.25° (tropical cyclone-permitting). Analysis has been confined to the contiguous United States (CONUS). It is shown that, for these models, integrating at higher atmospheric resolution improves all aspects of simulated extreme precipitation: spatial patterns, intensities and seasonal timing. In response to 2×CO2 concentrations, all models show a mean intensification of precipitation rates during extreme events of approximately 3-4% K−1. However, projected regional patterns of changes in extremes are dependent on model resolution. For example, the highest-resolution models show increased precipitation rates during extreme events in the hurricane season in the CONUS southeast, this increase is not found in the low-resolution model. These results emphasize that, for the study of extreme precipitation there is a minimum model resolution that is needed to capture the weather phenomena generating the extremes. Finally, the observed record and historical model experiments were used to investigate changes in the recent past. In part because of large intrinsic variability, no evidence was found for changes in extreme precipitation attributable to climate change in the available observed record.
This study demonstrates skillful seasonal prediction of 2m air temperature and precipitation over land in a new high-resolution climate model developed by Geophysical Fluid Dynamics Laboratory, and explores the possible sources of the skill. We employ a statistical optimization approach to identify the most predictable components of seasonal mean temperature and precipitation over land, and demonstrate the predictive skill of these components. First, we show improved skill of the high-resolution model over the previous lower-resolution model in seasonal prediction of NINO3.4 index and other aspects of interest. Then we measure the skill of temperature and precipitation in the high-resolution model for boreal winter and summer, and diagnose the sources of the skill. Lastly, we reconstruct predictions using a few most predictable components to yield more skillful predictions than the raw model predictions. Over three decades of hindcasts, we find that the two most predictable components of temperature are characterized by a component that is likely due to changes in external radiative forcing in boreal winter and summer, and an ENSO-related pattern in boreal winter. The most predictable components of precipitation in both seasons are very likely ENSO-related. These components of temperature and precipitation can be predicted with significant correlation skill at least 9 months in advance. The reconstructed predictions using only the first few predictable components from the model show considerably better skill relative to observations than raw model predictions. This study shows that the use of refined statistical analysis and a high-resolution dynamical model leads to significant skill in seasonal predictions of 2m air temperature and precipitation over land.
Wrzesien, M L., T M Pavelsky, and Sarah B Kapnick, et al., July 2015: Evaluation of snow cover fraction for regional climate simulations in the Sierra Nevada. International Journal of Climatology, 35(9), DOI:10.1002/joc.4136. Abstract
Mountain snow cover plays an important role in regional climate due to its high albedo, its effects on atmospheric convection, and its influence on runoff. Snowpack water storage is also a critical water resource and understanding how it varies is of great social value. Models are often employed to reconstruct snowpack and explore and understand snow cover variability. Here, we use a new, accurate satellite-derived snow product to evaluate the ability of the Weather Research and Forecasting (WRF) regional climate model, combined with the Noah land surface model with multi-parameterization options (Noah-MP), to simulate snow cover fraction (SCF) and snow water equivalent (SWE) in a 3-km domain over the central Sierra Nevada. WRF/Noah-MP SWE simulations improve on previous versions of the Noah land surface model by removing an early bias in snow melt, though a 2-day positive melt bias in SWE timing remains significant at the 90% confidence level. In addition, WRF/Noah-MP identifies the areas where snow is present to within 94.3% and captures large-scale variability in SCF. Temporal root mean squared error (RMSE) of the domain-average SCF was 1938.6 km2 (24%). However, our study shows that WRF/Noah-MP struggles to simulate SCF at finer spatial scales. The parameterization for SCF fails to produce temporal variations in grid-scale SCF, and depletion occurs too rapidly. As a result, the WRF/Noah-MP SCF parameterization reduces to a binary function in mountain environments. Sensitivity tests show that adjustment of the parameterization may improve simulation of SCF during accumulation or melt but does not remove the bias for the entire snow season. Although WRF/Noah-MP accurately simulates the presence or absence of snow, high-resolution, reliable SCF estimates may only be attainable if snow depletion parameterizations are designed specifically for complex topographical areas.
The seasonal predictability of extratropical storm tracks in Geophysical Fluid Dynamics Laboratory (GFDL)’s high-resolution climate model has been investigated using an average predictability time analysis. The leading predictable components of extratropical storm tracks are ENSO-related spatial pattern for both boreal winter and summer, and the second predictable components are mostly due to changes in external radiative forcing and multidecadal oceanic variability. These two predictable components for both seasons show significant correlation skill for all leads from 0 to 9 months, while the skill of predicting the boreal winter storm track is consistently higher than that of the austral winter. The predictable components of extratropical storm tracks are dynamically consistent with the predictable components of the upper troposphere jet flow for both seasons. Over the region with strong storm track signals in North America, the model is able to predict the changes in statistics of extremes connected to storm track changes (e.g., extreme low and high sea level pressure and extreme 2m air temperature) in response to different ENSO phases. These results point towards the possibility of providing skillful seasonal predictions of the statistics of extratropical extremes over land using high-resolution coupled models.
The high mountains of Asia, including the Karakoram, Himalayas and Tibetan Plateau, combine to form a region of perplexing hydroclimate changes. Glaciers have exhibited mass stability or even expansion in the Karakoram region1, 2, 3, contrasting with glacial mass loss across the nearby Himalayas and Tibetan Plateau1, 4, a pattern that has been termed the Karakoram anomaly. However, the remote location, complex terrain and multi-country fabric of high-mountain Asia have made it difficult to maintain longer-term monitoring systems of the meteorological components that may have influenced glacial change. Here we compare a set of high-resolution climate model simulations from 1861 to 2100 with the latest available observations to focus on the distinct seasonal cycles and resulting climate change signatures of Asia’s high-mountain ranges. We find that the Karakoram seasonal cycle is dominated by non-monsoonal winter precipitation, which uniquely protects it from reductions in annual snowfall under climate warming over the twenty-first century. The simulations show that climate change signals are detectable only with long and continuous records, and at specific elevations. Our findings suggest a meteorological mechanism for regional differences in the glacier response to climate warming.
Tropical cyclones (TCs) are a hazard to life and property and a prominent element of the global climate system, therefore understanding and predicting TC location, intensity and frequency is of both societal and scientific significance. Methodologies exist to predict basin-wide, seasonally-aggregated TC activity months, seasons and even years in advance. We show that a newly developed high-resolution global climate model can produce skillful forecasts of seasonal TC activity on spatial scales finer than basin-wide, from months and seasons in advance of the TC season. The climate model used here is targeted at predicting regional climate and the statistics of weather extremes on seasonal to decadal timescales, and is comprised of high-resolution (50km×50km) atmosphere and land components, and more moderate resolution (~100km) sea ice and ocean components. The simulation of TC climatology and interannual variations in this climate model is substantially improved by correcting systematic ocean biases through “flux-adjustment.” We perform a suite of 12-month duration retrospective forecasts over the 1981-2012 period, after initializing the climate model to observationally-constrained conditions at the start of each forecast period – using both the standard and flux-adjusted versions of the model. The standard and flux-adjusted forecasts exhibit equivalent skill at predicting Northern Hemisphere TC season sea surface temperature, but the flux-adjusted model exhibits substantially improved basin-wide and regional TC activity forecasts, highlighting the role of systematic biases in limiting the quality of TC forecasts. These results suggest that dynamical forecasts of seasonally-aggregated regional TC activity months in advance are feasible.
This study assesses the ability of a newly developed high-resolution coupled model from the Geophysical Fluid Dynamics Laboratory to simulate the cold-season hydroclimate in the present climate, and examines its response to climate change forcing. Output is assessed from a 280-yr control simulation based on 1990 atmospheric composition and an idealized 140-yr future simulation where atmospheric CO2 increases at 1% yr−1 until doubling in year 70 and then remains constant.
When compared to a low-resolution model, the high-resolution model is found to better represent the geographic distribution of snow variables in the present climate. In response to idealized radiative forcing changes, both models produce similar global-scale responses where global-mean temperature and total precipitation increase while snowfall decreases. Zonally, snowfall tends to decrease in the low to mid latitudes and increase in the mid to high latitudes.
At the regional scale, the high and low-resolution models sometimes diverge in the sign of projected snowfall changes; the high-resolution model exhibits future increases in a few select high altitude regions, notably the northwestern Himalaya region and small regions in the Andes and southwestern Yukon. Despite such local signals, there is an almost universal reduction in snowfall as a percent of total precipitation in both models. Using a simple multivariate model, temperature is shown to drive these trends by decreasing snowfall almost everywhere while precipitation increases snowfall in the high altitudes and mid to high latitudes. Mountainous regions of snowfall increases in the high-resolution model exhibit a unique dominance of the positive contribution from precipitation over temperature.
Pavelsky, T M., S Sobolowski, and Sarah B Kapnick, et al., September 2012: Changes in orographic precipitation patterns caused by a shift from snow to rain. Geophysical Research Letters, 39, L18706, DOI:10.1029/2012GL052741. Abstract
Climate warming will likely cause a shift from snow to
rain in midlatitude mountains. Because rain falls faster than
snow, it is not advected as far by prevailing winds before
reaching the ground. A shift in precipitation phase thus may
alter precipitation patterns. Using the Weather Research and
Forecasting (WRF) regional climate model at 27-9-3 km
resolutions over the California Sierra Nevada, we conducted
an idealized experiment consisting of a present climate
control run and two additional simulations in which (a) fall
speed for snow is similar to rain and (b) all precipitation is
constrained to fall as liquid. Rather than simulating future
climates directly, these perturbation experiments allow us to
test the potential impacts of changing precipitation phase in
isolation from other factors such as variable large-scale
atmospheric circulation. Relative to the control, both perturbations
result in a rain shadow deepened by 30–60%,
with increased focusing of precipitation on the western
Sierra Nevada slopes best resolved at ≤9 km resolutions. Our
results suggest that altered precipitation phase associated
with climate change will likely affect spatial distributions of
water resources, floods, and landslides in the Sierra Nevada
and similar midlatitude mountain ranges.
