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1. Relationship Between Tropical Cloud Feedback and Climatological Bias in Clouds

   https://doi.org/10.1029/2024GL111347  

   Thackeray, Chad W; Zelinka, Mark D; Norris, Jesse; Hall, Alex; Po‐Chedley, Stephen (December 2024, Geophysical Research Letters)

   Abstract Global climate model (GCM) projections of future climate are uncertain largely due to a persistent spread in cloud feedback. This is despite efforts to reduce this model uncertainty through a variety of emergent constraints (ECs); with several studies suggesting an important role for present‐day biases in clouds. Here, we use three generations of GCMs to assess the value of climatological cloud metrics for constraining uncertainty in cloud feedback. We find that shortwave cloud radiative properties across the Southern Hemisphere extratropics are most robustly correlated with tropical cloud feedback (TCF). Using this relationship in conjunction with observations, we produce an EC that yields a TCF value of 0.52 ± 0.34 W/m²/K, which equates to a 34% reduction in uncertainty. Thus, we show that climatological cloud properties can be used to reduce uncertainty in how clouds will respond to future warming. 

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2. Temperature and precipitation projections for the Antarctic Peninsula over the next two decades: contrasting global and regional climate model simulations

   https://doi.org/https://doi.org/10.1007/s00382-021-05667-2  

   Bozkurt, D. (February 2021, Climate dynamics)

   null (Ed.)

   This study presents near future (2020–2044) temperature and precipitation changes over the Antarctic Peninsula under the high-emission scenario (RCP8.5). We make use of historical and projected simulations from 19 global climate models (GCMs) participating in Coupled Model Intercomparison Project phase 5 (CMIP5). We compare and contrast GCMs projections with two groups of regional climate model simulations (RCMs): (1) high resolution (15-km) simulations performed with Polar-WRF model forced with bias-corrected NCAR-CESM1 (NC-CORR) over the Antarctic Peninsula, (2) medium resolution (50-km) simulations of KNMI-RACMO21P forced with EC-EARTH (EC) obtained from the CORDEX-Antarctica. A further comparison of historical simulations (1981–2005) with respect to ERA5 reanalysis is also included for circulation patterns and near-surface temperature climatology. In general, both RCM boundary conditions represent well the main circulation patterns of the historical period. Nonetheless, there are important differences in projections such as a notable deepening and weakening of the Amundsen Sea Low in EC and NC-CORR, respectively. Mean annual near-surface temperatures are projected to increase by about 0.5–1.5 ◦ C across the entire peninsula. Temperature increase is more substantial in autumn and winter ( ∼ 2 ◦C). Following opposite circulation pattern changes, both EC and NC-CORR exhibit different warming rates, indicating a possible continuation of natural decadal variability. Although generally showing similar temperature changes, RCM projections show less warming and a smaller increase in melt days in the Larsen Ice Shelf compared to their respective driving fields. Regarding precipitation, there is a broad agreement among the simulations, indicating an increase in mean annual precipitation ( ∼ 5 to 10%). However, RCMs show some notable differences over the Larsen Ice Shelf where total precipitation decreases (for RACMO) and shows a small increase in rain frequency. We conclude that it seems still difficult to get consistent projections from GCMs for the Antarctic Peninsula as depicted in both RCM boundary conditions. In addition, dominant and common changes from the boundary conditions are largely evident in the RCM simulations. We argue that added value of RCM projections is driven by processes shaped by finer local details and different physics schemes that are introduced by RCMs, particularly over the Larsen Ice Shelf. 

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3. A comparison of multiple statistically downscaled climate change datasets for the conterminous USA

   https://doi.org/10.1088/2515-7620/aca3ee  

   Kim, John B; Jiang, Yueyang; Hawkins, Linnia R; Still, Christopher J (November 2022, Environmental Research Communications)

   Abstract Climate change projections provided by global climate models (GCM) are generally too coarse for local and regional applications. Local and regional climate change impact studies therefore use downscaled datasets. While there are studies that evaluate downscaling methodologies, there is no study comparing the downscaled datasets that are actually distributed and used in climate change impact studies, and there is no guidance for selecting a published downscaled dataset. We compare five widely used statistically downscaled climate change projection datasets that cover the conterminous USA (CONUS): ClimateNA, LOCA, MACAv2-LIVNEH, MACAv2-METDATA, and NEX-DCP30. All of the datasets are derived from CMIP5 GCMs and are publicly distributed. The five datasets generally have good agreement across CONUS for Representative Concentration Pathways (RCP) 4.5 and 8.5, although the agreement among the datasets vary greatly depending on the GCM, and there are many localized areas of sharp disagreements. Areas of higher dataset disagreement emerge over time, and their importance relative to differences among GCMs is comparable between RCP4.5 and RCP8.5. Dataset disagreement displays distinct regional patterns, with greater disagreement in △Tmax and △Tmin in the interior West and in the North, and disagreement in △P in California and the Southeast. LOCA and ClimateNA are often the outlier dataset, while the seasonal timing of ClimateNA is somewhat shifted from the others. To easily identify regional study areas with high disagreement, we generated maps of dataset disagreement aggregated to states, ecoregions, watersheds, and forests. Climate change assessment studies can use the maps to evaluate and select one or more downscaled datasets for their study area. 

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4. Convection-permitting simulations of historical and possible future climate over the contiguous United States

   https://doi.org/10.1007/s00382-022-06306-0  

   Gensini, Vittorio A.; Haberlie, Alex M.; Ashley, Walker S. (May 2022, Climate Dynamics)

   Abstract This study presents a novel, high-resolution, dynamically downscaled dataset that will help inform regional and local stakeholders regarding potential impacts of climate change at the scales necessary to examine extreme mesoscale conditions. WRF-ARW version 4.1.2 was used in a convection-permitting configuration (horizontal grid spacing of 3.75 km; 51 vertical levels; data output interval of 15-min) as a regional climate model for a domain covering the contiguous US Initial and lateral boundary forcing for the regional climate model originates from a global climate model simulation by NCAR (Community Earth System Model) that participated in phase 5 of the Coupled Model Inter comparison Project. Herein, we use a version of these data that are regridded and bias corrected. Two 15-year downscaled simulation epochs were examined comprising of historical (HIST; 1990–2005) and potential future (FUTR; 2085–2100) climate using Representative Concentration Pathway (RCP) 8.5. HIST verification against independent observational data revealed that annual/seasonal/monthly temperature and precipitation (and their extremes) are replicated admirably in the downscaled HIST epoch, with the largest biases in temperature noted with daily maximum temperatures (too cold) and the largest biases in precipitation (too dry) across the southeast US during the boreal warm season. The simulations herein are improved compared to previous work, which is significant considering the differences in previous modeling approaches. Future projections of temperature under the RCP 8.5 scenario are consistent with previous works using various methods. Future precipitation projections suggest statistically significant decreases of precipitation across large segments of the southern Great Plains and Intermountain West, whereas significant increases were noted in the Tennessee/Ohio Valleys and across portions of the Pacific Northwest. Overall, these simulations serve as an additional datapoint/method to detect potential future changes in extreme meso-γ weather phenomena. 

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5. Multivariate bias correction and downscaling of climate models with trend-preserving deep learning

   https://doi.org/10.1007/s00382-024-07406-9  

   Wang, Fang; Tian, Di (October 2024, Climate Dynamics)

   Abstract Global climate models (GCMs) and Earth system models (ESMs) exhibit biases, with resolutions too coarse to capture local variability for fine-scale, reliable drought and climate impact assessment. However, conventional bias correction approaches may cause implausible climate change signals due to unrealistic representations of spatial and intervariable dependences. While purely data-driven deep learning has achieved significant progress in improving climate and earth system simulations and predictions, they cannot reliably learn the circumstances (e.g., extremes) that are largely unseen in historical climate but likely becoming more frequent in the future climate (i.e., climate non-stationarity). This study shows an integrated trend-preserving deep learning approach that can address the spatial and intervariable dependences and climate non-stationarity issues for downscaling and bias correcting GCMs/ESMs. Here we combine the super-resolution deep residual network (SRDRN) with the trend-preserving quantile delta mapping (QDM) to downscale and bias correct six primary climate variables at once (including daily precipitation, maximum temperature, minimum temperature, relative humidity, solar radiation, and wind speed) from five state-of-the-art GCMs/ESMs in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We found that the SRDRN-QDM approach greatly reduced GCMs/ESMs biases in spatial and intervariable dependences while significantly better-reducing biases in extremes compared to deep learning. The estimated drought based on the six bias-corrected and downscaled variables captured the observed drought intensity and frequency, which outperformed state-of-the-art multivariate bias correction approaches, demonstrating its capability for correcting GCMs/ESMs biases in spatial and multivariable dependences and extremes. 

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