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1. Revisiting the “East African Paradox”: CMIP6 Models Also Struggle to Reproduce Strong Observed Long Rain Drying Trends

   https://doi.org/10.1175/JCLI-D-24-0225.1  

   Schwarzwald, Kevin ; Seager, Richard ( December 2024 , Journal of Climate)

   Societies in much of the Horn of Africa are affected by variability in two distinct rainy seasons: the March–May (MAM) “long” rains and the October–December (OND) “short” rains. A recent five-season, La Niña–forced drought has renewed concerns about possible anthropogenic drying trends in the long rains, which had partially recovered after a multidecadal drying trend in the 1980s through the 2000s. Despite observed drying, previous generations of global climate models (GCMs) have consistently projected long-term wetting due to increased greenhouse gas concentrations, an East African “paradox” which complicates the interpretation of East African rainfall projections. We investigate the paradox in new phase 6 of the Coupled Model Intercomparison Project (CMIP6) and seasonal forecast models, leveraging an improved observational record and large ensembles to better differentiate internal and forced trends. We find observed drying trends are at the limits of the GCM spread during the peak paradox period, though the recent recovery is comfortably within the model spread. We find that the apparent paradox is largely removed by prescribing sea surface temperatures (SSTs) and is likely caused by the GCM difficulties in simulating observed tropical Pacific SST trends in recent decades. In line with arguments that these SST trends are at least partially forced anthropogenically, we recommend users of future rainfall projections in East Africa consider the possibility of long-term MAM drying despite GCM wetting and call for future model simulations that better sample the expected spread of SSTs.

   Societies in the Horn of Africa depend on the March–May “long” rains and the September–December “short” rains to support agricultural and pastoral practices on rain-fed lands. Recent major droughts have raised worries about possible anthropogenically forced drying trends in the long rains through global climate models (GCMs) project wetting. This East African “paradox” complicates long-term climate adaptation planning. We find the paradox continues in the newest generation of GCMs and seasonal forecast models; though a recent recovery in rainfall is well-captured, the strongest observed drying trends are rare in simulations. The paradox likely arises from known GCM biases in the Pacific Ocean interacting with natural variability. We recommend that researchers and policymakers consider possible long-term drying despite GCM projections of wetting.

    

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2. Fidelity of global climate models in representing the horizontal water vapour transport

   https://doi.org/10.1002/joc.6546  

   Hari, Vittal ; Villarini, Gabriele ; Zhang, Wei ( March 2020 , International Journal of Climatology)

   The horizontal water vapour transport (IVT) is one of the key variables connected to precipitation extremes and floods, especially in the mid‐latitudinal countries, and represents a key link between water sources and sink regions. Because of its significant impacts, great efforts have been made to examine IVT and its projected changes in response to changes to the climate system, leveraging outputs from global climate models (GCMs). However, to gain more confidence in the projections, it is important to evaluate how well the GCMs can reproduce the historical past. Here, we assess how well 10 GCMs archived in the Climate Model Intercomparison Project Phase 5 can reproduce the spatial and temporal patterns of the mean and 85th percentile of the IVT distribution. Analyses are performed at the global scale using outputs at the 6‐hourly resolution, and four different reanalysis products as reference. We find that the results from the GCMs are in good agreement with the reanalyses in terms of the IVT climatology; however, the GCMs fail to capture the trends in IVT, with almost all the models showing a negative correlation with the reanalysis data for both the mean and the 85th percentile. We also extend our analyses to the daily time scale, reaching the same conclusions drawn for the 6‐hourly resolution. These results further highlight the need for a careful evaluation of the GCM outputs in reproducing the observed IVT distribution.

    

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3. Projecting the Most Likely Annual Urban Heat Extremes in the Central United States

   https://doi.org/10.3390/atmos10120727  

   Jahn, David E. ; Gallus, William A. ; Nguyen, Phong T. ; Pan, Qiyun ; Cetin, Kristen ; Byon, Eunshin ; Manuel, Lance ; Zhou, Yuyu ; Jahani, Elham ( December 2019 , Atmosphere)

   Climate studies based on global climate models (GCMs) project a steady increase in annual average temperature and severe heat extremes in central North America during the mid-century and beyond. However, the agreement of observed trends with climate model trends varies substantially across the region. The present study focuses on two different locations: Des Moines, IA and Austin, TX. In Des Moines, annual extreme temperatures have not increased over the past three decades unlike the trend of regionally-downscaled GCM data for the Midwest, likely due to a “warming hole” over the area linked to agricultural factors. This warming hole effect is not evident for Austin over the same time period, where extreme temperatures have been higher than projected by regionally-downscaled climate (RDC) forecasts. In consideration of the deviation of such RDC extreme temperature forecasts from observations, this study statistically analyzes RDC data in conjunction with observational data to define for these two cities a 95% prediction interval of heat extreme values by 2040. The statistical model is constructed using a linear combination of RDC ensemble-member annual extreme temperature forecasts with regression coefficients for individual forecasts estimated by optimizing model results against observations over a 52-year training period. 

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4. The Mesoscale Response to Global Warming over the Pacific Northwest Evaluated Using a Regional Climate Model Ensemble

   https://doi.org/10.1175/JCLI-D-21-0061.1  

   Mass, Clifford F. ; Salathé, Eric P ; Steed, Richard ; Baars, Jeffrey ( March 2022 , Journal of Climate)

   Abstract This paper describes the downscaling of an ensemble of 12 general circulation models (GCMs) using the Weather Research and Forecasting (WRF) Model at 12-km grid spacing over the period 1970–2099, examining the mesoscale impacts of global warming as well as the uncertainties in its mesoscale expression. The RCP8.5 emissions scenario was used to drive both global and regional climate models. The regional climate modeling system reduced bias and improved realism for a historical period, in contrast to substantial errors for the GCM simulations driven by lack of resolution. The regional climate ensemble indicated several mesoscale responses to global warming that were not apparent in the global model simulations, such as enhanced continental interior warming during both winter and summer as well as increasing winter precipitation trends over the windward slopes of regional terrain, with declining trends to the lee of major barriers. During summer there is general drying, except to the east of the Cascades. The 1 April snowpack declines are large over the lower-to-middle slopes of regional terrain, with small snowpack increases over the lower elevations of the interior. Snow-albedo feedbacks are very different between GCM and RCM projections, with the GCMs producing large, unphysical areas of snowpack loss and enhanced warming. Daily average winds change little under global warming, but maximum easterly winds decline modestly, driven by a preferential sea level pressure decline over the continental interior. Although temperatures warm continuously over the domain after approximately 2010, with slight acceleration over time, occurrences of temperature extremes increase rapidly during the second half of the twenty-first century. Significance Statement This paper provides a unique high-resolution view of projected climate change over the Pacific Northwest and does so using an ensemble of regional climate models, affording a look at the uncertainties in local impacts of global warming. The paper examines regional meteorological processes influenced by global warming and provides guidance for adaptation and preparation. 

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5. Framework for using downscaled climate model projections in ecological experiments to quantify plant and soil responses

   https://doi.org/10.1002/ecs2.2857  

   Owen, Rachel_K ; Webb, Elisabeth_B ; Goyne, Keith_W ; Svoma, Bohumil_M ; Gautam, Sagar ( September 2019 , Ecosphere)

   Soil and plant responses to climate change can be quantified in controlled settings. However, the complexity of climate projections often leads researchers to evaluate ecosystem response based on general trends, rather than specific climate model outputs. Climate projections capture spatial and temporal climate extremes and variability that are lost when using mean climate trends. In addition, application of climate projections in experimental settings remains limited. Our objective was to develop a framework to incorporate statistically downscaled climate model projections into the design of temperature and precipitation treatments for ecological experiments. To demonstrate the utility of experimental treatments derived from climate projections, we used wetlands in the Great Plains as a model ecosystem for evaluating plant and soil responses. Spatial and temporal projections were selected to capture variability and intensity of projected future conditions for exemplary purposes. To illustrate climate projection application for ecological experiments, we developed temperature and precipitation treatments based on moderate‐emissions scenario climate outputs (i.e.,RCP4.5–650 ppmCO₂equivalent). Our temperature treatments captured weekly trends that represented cool, average, and warm temperature predictions, and our daily precipitation treatments mimicked various seasonal precipitation trends and extreme events projected for the late 21st century. Treatments were applied to two short‐term controlled experiments evaluating (1) plant germination (temperature treatment applied in growth chamber) and (2) soil nitrogen cycling (precipitation treatment applied in greenhouse) responses to projected future conditions in the Great Plains. Our approach provides flexibility for selecting appropriate and precise climate model outputs to design experimental treatments. Using these techniques, ecologists can better incorporate variation in climate model projections for experimentally evaluating ecosystem responses to future climate conditions, reduce uncertainty in predictive ecological models, and apply predicted outcomes when making management and policy decisions.

    

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