Precipitation Projections Research Articles

Multi-scenario simulation of runoff and nutrient loads in a rapidly urbanizing watershed during China's Dual Carbon periods

Apprehending the hydrological and nutrient variations in rapidly urbanizing watersheds under changing environments is crucial for pollution control and water resource management. However, existing studies have primarily focused on hydrological processes, neglecting water quality aspects, and comprehensive assessment of future runoff and nutrient loads in these watersheds during China's Dual Carbon periods is limited. This study firstly bridges these gaps by constructing multi-scenario with different levels of “Urban Development - Ecological Conservation” and utilizing latest bias-corrected General Circulation Models or Global Climate Models (GCMs) projections to evaluate future runoff and nutrient loads in the Shenzhen River. The calibrated and validated models display satisfactory performance in simulating runoff, nutrient loads, and land use types. The bias-corrected GCMs projections exhibit enhanced accuracy for temperature variables, particularly during the wet season. Implementing effective ecological protection measures is paramount in mitigating water quantity fluctuations and controlling total nitrogen pollution, which is closely associated with urban development and human activities. Conversely, total phosphorus loads demonstrate greater simulation uncertainty, particularly during the dry season of the Carbon Neutrality period, requiring further exploration. Compared to the baseline period, runoff changes minimally, with notable seasonal variations. The findings highlight the escalating uncertainty in load predictions as time progresses. Additionally, addressing uncertainties in precipitation projections driven by GCMs is imperative, given their substantial influence on runoff and nutrient load simulations, particularly during challenging dry seasons. While further research is needed to reduce simulation uncertainty, our study provides valuable insights into nitrogen-phosphorus pollution control and sustainable water resource management in rapidly urbanizing watersheds, especially during the near-term period.

Emergent constraints on future extreme precipitation intensification: from global to continental scales

Extreme precipitation intensification due to global warming has received significant attention; however, large uncertainties remain regarding how much it will change in the future, even under a given greenhouse gas emissions pathway. Our constraining analysis provides smaller future extreme precipitation intensification with reduced uncertainties than raw/unconstrained model simulation projections from the global to continental scales and also several sub-continental areas. Historical warming trends in climate model simulations present strong positive inter-model correlations with future annual maximum daily precipitation intensification from the global to continental scales. This emergent relationship is employed for constraining analysis. The proposed emergent constraints are mostly associated with thermodynamics (atmospheric moisture changes), with limited contribution from dynamics (atmospheric circulation variations). This constraining framework has various advantages, including lower observation uncertainty, and more robust theoretical interpretation than previously suggested constraints for extreme precipitation frequency projections. CMIP6 models generally overestimate historically observed warming, which resultantly generates weaker extreme precipitation intensification following constraining analysis than unconstrained model simulation projections from the global to individual continents, and even several mid- and high-latitude sub-continental areas. Additionally, constrained projections exhibit narrower uncertainty ranges in future extreme precipitation projections. During the late 21st century (2070–2099), under the high-emission scenario (Shared Socioeconomic Pathway 5–8.5, characterized by fossil fueled development and a radiative forcing of 8.5 W m−2 in 2100), our results present reduced global average intensity and uncertainty for future annual maximum daily precipitation intensification by 25% and 27%, respectively.

Predicting future conservation areas while avoiding sympatry in two alpine amphibians severely threatened by climate change

Climate change will cause important declines in environmental suitability, especially when living at high elevations. The critically endangered Salamandra lanzai from the Alps may be severely exposed to future climate change effects and its suitable climate niche may shrink or shift. Another Alpine salamander (S. atra) is present in the region, which in case of spatial overlap may represent a risk for S. lanzai. It is urgent to estimate the effect of future climate change on these species and identify priority areas for conservation while preventing their sympatry. With a species distribution modelling (SDM) approach, we projected the current and future climate suitability of both salamander species. We accounted for uncertainty related to the methods (model replicates) and climate projections (data source, global circulation model and scenario) to provide a consensus map for practitioners. This map also takes into account potential sympatry with S. atra by penalizing the suitability scores of S. lanzai by the scores of S. atra. We predict a severe effect of climate change on both species distributions. Most of the current habitats are projected to become largely unsuitable by 2070, regardless of the climatology and scenario. We identified important spatial disagreements between projections based on different data sources, mostly due to precipitation projections and daily temperature variation. This highlights the need to account for multiple climatologies in mountainous environments. The habitat of both species is highly fragmented, which is expected to limit distributional shifts through natural dispersion. We suggest to explore the possibility of translocation for the most threatened populations and simultaneously develop captive breeding programs. Biotic interactions are rarely accounted for in SDMs, and we encourage the documentation of species with similar ecological requirements to improve the relevance of SDMs for future conservation planning.

Spatiotemporal Projections of Precipitation in the Lancang–Mekong River Basin Based on CMIP6 Models

The Lancang–Mekong River Basin (LMRB) is the largest international river in Southeast Asia, supporting a population of about 70 million people. Precipitation is the main source of water resources in the basin, with significant impacts on ecology, production, and livelihoods in the basin. In this study, future precipitation was projected using the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models. The initial bias of each model was corrected using the daily bias-correction (DBC) method, and then the models were ensembled using the Bayesian model-averaging (BMA) method. The evaluation, based on metrics such as climatology bias, root-mean-square error (RMSE), mean absolute error (MAE), and correlation coefficient (COR), showed that the ensemble precipitation performs better than the individual models. Precipitation under four future Shared Socioeconomic Pathway scenarios (SSP126, SSP245, SSP370, SSP585) displayed an increasing trend throughout the LMRB. The anomalies in annual precipitation in 2061–2090 under each scenario are 136 mm, 142 mm, 114 mm, and 227 mm, in that order. Precipitation in spring and winter shows a trend of increasing in the northern LMRB and decreasing in the southern LMRB, and precipitation in summer and autumn shows a significant trend of increasing in almost the whole basin (significance level 0.05). Spring precipitation in the Mekong Delta decreases in all scenarios. The ratio of wet-season precipitation to dry-season precipitation shows an increasing trend for all scenarios, indicating that the difference between wet-season precipitation and dry-season precipitation will increase in the future. For daily precipitation, the Lancang River Basin (LRB) is dominated by a 3–5% increase in the number of days with 5–10 mm/d of precipitation and the Mekong River Basin (MRB) by a 3–5% increase in the number of days with 10–20 mm/d of precipitation under four SSP scenarios in 2061–2090. There are important changes in the spatial distribution of future precipitation, with the 2500 mm isohyet expanding outwards in a circular pattern and the center of the 1500 mm isohyet moving westwards; i.e., areas with annual precipitation exceeding 2500 mm and 1500 mm will expand. For dry-season precipitation, the 500 mm isohyet shrinks, mainly in a circular pattern towards the center, while the 300 mm isohyet moves mainly towards the east, indicating that areas of dry-season precipitation below 500 mm and 300 mm will expand. In the future, the LMRB will generally become wetter in the wet season and drier in the dry season.

Evaluation and Projection of Climate Change in the Second Songhua River Basin Using CMIP6 Model Simulations

The aim of this study is to evaluate the performance of the Global Climate Model (GCM) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in historical simulations of temperature and precipitation. The goal is to select the best performing GCMs for future projection of temperature and precipitation in the Second Songhua River Basin under multiple shared socioeconomic pathways (SSPs). Interannual variability skill (IVS) and Taylor diagrams are used to evaluate the spatiotemporal performance of GCMs against temperature and precipitation data published by the China Meteorological Science Commons during 1956–2016. In addition, five relatively independent models are selected to simulate the temperature and precipitation for 2021–2050 using Hierarchical Clustering. The selected models are CMCC-ESM2, EC-Earth3-Veg-LR, IPSL-CM6A-LR, MIROC-ES2L, and MPI-ESM1-2-HR. The projected results find that SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios show an increasing trend of future annual mean temperature and precipitation. However, for annual precipitation, there is a mixed state of increase and decrease among different models on the seasonal scale. In general, future temperature and precipitation changes still show a trend of growth and uneven distribution in the Second Songhua River Basin, which may be further accelerated by human activities.

A thicker Antarctic ice stream during the mid-Pliocene warm period

Ice streams regulate most ice mass loss in Antarctica. Determining ice stream response to warmer conditions during the Pliocene could provide insights into their future behaviour, but this is hindered by a poor representation of subglacial topography in ice-sheet models. We address this limitation using a high-resolution model for Dronning Maud Land (East Antarctica). We show that contrary to dynamic thinning of the region’s ice streams following ice-shelf collapse, the largest ice stream, Jutulstraumen, thickens by 700 m despite lying on a retrograde bed slope. We attribute this counterintuitive thickening to a shallower Pliocene subglacial topography and inherent high lateral stresses at its flux gate. These conditions constrict ice drainage and, combined with increased snowfall, allow ice accumulation upstream. Similar stress balances and increased precipitation projections occur across 27% of present-day East Antarctica, and understanding how lateral stresses regulate ice-stream discharge is necessary for accurately assessing Antarctica’s future sea-level rise contribution.

Climate change impacts on hydrometeorological and river hydrological extremes in Quito, Ecuador

Study regionQuito, Ecuador Study focusThe study region faces two water-related challenges, which, to date, have only been studied to a minimal extent: extreme precipitation events and water shortage in the dry season. This study investigates the current conditions and future changes in short-duration events, low river discharges and associated uncertainties. Daily precipitation and temperature projections from 19 state-of-the-art global climate models (CMIP6) and four future scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5) are downscaled with delta change and more advanced quantile perturbation methodologies. Temporal disaggregation is applied to obtain sub-daily precipitation with five extreme value distributions (EVDs). Three conceptual hydrological models are implemented to quantify the impacts on river flows. Variance decomposition is applied to estimate the uncertainty share of climate models, hydrological models, and EVDs in the results. New hydrological insightsThe results indicate an intensification of extreme precipitation events, with short-duration events expected to be more intense by up to 30% in the near future (2021–2050) and up to 60% in the far future (2070–2099). The river peak discharges are projected to increase by 5–20% and 10–50% in the near and far future, respectively. On the contrary, the low river flows are projected to decrease by 0–13% and 0–30% for the respective time horizons. Overall, climate models are the dominant source of uncertainty.

Evaluation and Projection of Precipitation in CMIP6 Models over the Qilian Mountains, China

The Qilian Mountains (QMs) act as the “water tower” of the Hexi Corridors, playing an important role in the regional ecosystem security and economic development. Therefore, it is of great significance to understand the spatiotemporal characteristics of precipitation in the QMs. This study evaluated the performance of 21 models of phase 6 of the Coupled Model Intercomparison Project (CMIP6) from 1959 to 1988 based on ERA5 and in situ datasets. In addition, the precipitation changing trend from 2015 to 2100 was projected according to four shared socioeconomic pathways (SSPs): namely, SSP126, SSP245, SSP370, and SSP585. The results have shown the following: (1) all CMIP6 models could reflect the same precipitation changing trend, based on the observed datasets (−2.01 mm·10a−1), which was slightly lower than that of ERA5 (2.82 mm·10a−1). Multi-mode ensemble averaging (MME) showed that the projected precipitation-change trend of the four scenarios was 5.73, 9.15, 12.23, and 16.14 mm·10a−1, respectively. (2) The MME and ERA5 showed the same precipitation spatial pattern. Also, during the period 1959–1988, the MME in spring, summer, autumn and winter was 130.07, 224.62, 95.96, and 29.07 mm, respectively, and that of ERA5 was 98.57, 280.77, 96.85, and 22.64 mm, respectively. The largest precipitation difference in summer was because of strong convection and variable circulation. (3) From 2015 to 2100, the snow-to-rain ratio was between 0.1 and 1.1, and the snow-to-rain ratio climate tendency rate was concentrated in the range of −10~0.1 mm·10a−1. Both of these passed the significance test (p < 0.05). The projected rainfall of all four SSPs all showed an increasing trend with values of 6.20, 11.31, 5.64, and 20.41 mm·10a−1, respectively. The snowfall of the four SSPs all showed a decreasing trend with values of 0.42, 2.18, 3.34, and 4.17 mm·10a−1, respectively.

A stacked ensemble learning and non‐homogeneous hidden Markov model for daily precipitation downscaling and projection

AbstractGlobal circulation models (GCMs) are routinely used to project future climate conditions worldwide, such as temperature and precipitation. However, inputs with a finer resolution are required to drive impact‐related models at local scales. The non‐homogeneous hidden Markov model (NHMM) is a widely used algorithm for the precipitation statistical downscaling for GCMs. To improve the accuracy of the traditional NHMM in reproducing spatiotemporal precipitation features of specific geographic sites, especially extreme precipitation, we developed a new precipitation downscaling framework. This hierarchical model includes two levels: (1) establishing an ensemble learning model to predict the occurrence probabilities for different levels of daily precipitation aggregated at multiple sites and (2) constructing a NHMM downscaling scheme of daily amount at the scale of a single rain gauge using the outputs of ensemble learning model as predictors. As the results obtained for the case study in the central‐eastern China (CEC), show that our downscaling model is highly efficient and performs better than the NHMM in simulating precipitation variability and extreme precipitation. Finally, our projections indicate that CEC may experience increased precipitation in the future. Compared with ~26 years (1990–2015), the extreme precipitation frequency and amount would significantly increase by 21.9%–48.1% and 12.3%–38.3%, respectively, by the late century (2075–2100) under the Shared Socioeconomic Pathway 585 climate scenario.

Applicability of CMIP5 and CMIP6 Models in China: Reproducibility of Historical Simulation and Uncertainty of Future Projection

Abstract General circulation model (GCM) outputs archived by the Coupled Model Intercomparison Project (CMIP) are widely used for climate projection. A comprehensive evaluation of the applicability of GCMs is crucial for generating reliable projections. However, previous GCM evaluation studies mainly focused on the reproducibility of the historical simulation to the observation data, ignoring the uncertainty involved in the future climate projection. In this study, an innovative and comprehensive index, named the history–future applicability score (HFAS), is proposed for evaluating the applicability of GCMs. Through incorporating the Taylor skill score (S) and the analysis of variance (ANOVA) within a general framework, the HFAS can reflect both the historical reproducibility and future uncertainty of GCMs. The HFAS framework is applied to quantitatively evaluating the skills of 28 GCMs (14 from CMIP5 and 14 from CMIP6) in modeling temperature and precipitation at 631 reference sites across China. Results indicate that (i) both CMIP5 and CMIP6 models underestimate the historical temperature of China and overestimate the historical precipitation; (ii) the total uncertainties in future temperature and precipitation projections increase along with time; compared to CMIP5, temperature uncertainty of CMIP6 is narrowed, while the precipitation uncertainty is increased; (iii) taking into account historical reproducibility and future uncertainty, CMIP6 models generally have increased applicability compared to CMIP5; and (iv) the best-performing GCMs in modeling temperature and precipitation of China are ACCESS-CM2 and MPI-ESM-LR, respectively, with the HFAS being 68.4 and 63.5. The findings are helpful for researchers to select locally appropriate GCMs and generate reliable regional climate projection.

Extreme Precipitation Events Variation and Projection in the Lancang-Mekong River Basin Based on CMIP6 Simulations

In recent years, global climate change causes more extreme precipitation events in Lancang-Mekong River Basin (Lancang-Mekong Basin). It leads to the increase of rainstorm and flood risk, which poses a threat to the flood control safety of Lancang-Mekong Basin. Based on the precipitation data of four global climate models of CMIP6, this paper selected six indexes, including PRCPTOT and R10, to study the change characteristics of the extreme precipitation indexes in the Lancang-Mekong Basin during 1980–2020 and predicted the development trend of the extreme precipitation indexes in the Lancang-Mekong Basin during 2021–2050. The results show that from 1980 to 2020, the extreme precipitation events in regions I and IV showed an increasing trend, while those in regions II and III showed a decreasing trend. From 2021 to 2050, the extreme precipitation events in all regions of the Lancang-Mekong River show an upward trend, and most indexes increased compared with the historical period. The extreme precipitation indexes in most regions increases the most under the scenario of SSP5-8.5, while the increase is small under the scenario of SSP2-4.5; The six extreme precipitation indexes selected in the paper can better reflect the trend of extreme precipitation events and floods. The trend of extreme precipitation indexes in the Lancang-Mekong River basin in the future is more significant, which indicates that it may be affected more frequently by extreme precipitation events and floods.

Projections of Mean and Extreme Precipitation Using the CMIP6 Model: A Study of the Yangtze River Basin in China

In this study, we conducted an analysis of the CN05.1 daily precipitation observation dataset spanning from 1985 to 2014. Subsequently, we ranked the 30 global climate model datasets within the NEX-GDDP-CMIP6 dataset using the RS rank score method. Multi-model weighted-ensemble averaging was then performed based on these RS scores, followed by a revision of the multi-model weighted-ensemble averaging (rs-MME) using the quantile mapping method. The revised rs-MME model data were utilized for simulating precipitation variations within the Yangtze River Basin. We specifically selected 11 extreme-precipitation indices to comprehensively evaluate the capability of the revised rs-MME model data in simulating extreme-precipitation occurrences in the region. Our investigation culminated in predicting the characteristics of precipitation and the potential shifts in extreme-precipitation patterns across the region under three distinct shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) for three temporal segments: the Near 21C (2021–2040), Mid 21C (2041–2070), and Late 21C (2071–2100). Our findings reveal that the revised rs-MME model data effectively resolve the issues of the overestimation and underestimation of precipitation data present in the previous model. This leads to an enhanced simulation of mean annual precipitation, the 95th percentile of precipitation, and the extreme-precipitation index for the historical period. However, there are shortcomings in the simulation of linear trends in mean annual precipitation, alongside a significant overestimation of the CWD and CDD indices. Furthermore, our analysis forecasts a noteworthy increase in future mean annual precipitation within the Yangtze River Basin region, with a proportional rise in forced radiation across varying scenarios. Notably, an ascending trend of precipitation is detected at the headwaters of the Yangtze River Basin, specifically under the Late 21C SSP5-8.5 scenario, while a descending trend is observed in other scenarios. Conversely, there is an escalating pattern of precipitation within the middle and lower reaches of the Yangtze River Basin, with most higher-rate changes situated in the middle reaches. Regarding extreme-precipitation indices, similar to the annual average precipitation, a remarkable upsurge is evident in the middle and lower reaches of the Yangtze River Basin, whereas a relatively modest increasing trend prevails at the headwaters of the Yangtze River Basin. Notably, the SSP5-8.5 scenario portrays a substantial increase in extreme-precipitation indices.

Future projection of extreme precipitation over the Korean Peninsula under global warming levels of 1.5 °C and 2.0 °C, using large ensemble of RCMs in CORDEX-East Asia Phase 2

Future projection of extreme precipitation over the Korean Peninsula under global warming levels of 1.5 °C and 2.0 °C, using large ensemble of RCMs in CORDEX-East Asia Phase 2

The Importance of Accounting for the North Atlantic Oscillation When Applying Observational Constraints to European Climate Projections

AbstractVariability in the North Atlantic Oscillation (NAO) has contributed to the recent multidecadal trends observed in European climate, especially to trends in winter precipitation over Northern Europe. However, the current generation of coupled climate models struggle to reproduce the NAO's contribution to multidecadal trends, which has important implications for deriving constraints based on the comparison of observed and modeled trends. An observational constraint based on attribution results, both with and without the contribution of variability associated with the NAO, is applied to projections of Northern European precipitation and temperature, and observed NAO variability is shown to lead to a constraint that overestimates future forced changes. Only after removing the NAO variability is the observed climate change consistent with model simulations, and a tighter, unbiased observational constraint based on the forced signal (without the NAO) can be applied to future projections.

Utility of Open-Access Long-Term Precipitation Data Products for Correcting Climate Model Projection in South China

Insufficient precipitation observations hinder the bias-correction of Global Climate Model (GCM) precipitation outputs in ungauged and remote areas. As a result, the reliability of future precipitation and water resource projections is restricted for these areas. Open-access quantitative precipitation estimation (QPE) products offer a potential solution to this challenge. This study assesses the effectiveness of three widely used, long-term QPEs, including ERA5, PERSIANN-CDR, and CHIRPS, in bias-correcting precipitation outputs from the CMIP6 GCMs. The evaluation involves the reproduction of precipitation distribution, streamflow simulation utility based on a hydrological model, and the accuracy of extreme indices associated with rainstorm/flood/drought events. This study selects the Beijiang basin located in the subtropical monsoon area of South China as the case study area. The results demonstrate that bias-correction using QPEs improves the performance of GCM precipitation outputs in reproducing precipitation/streamflow distribution and extreme indices, with a few exceptions. PCDR generally exhibits the most effective bias-correction utility, consistently delivering reasonable performance across various cases, making it a suitable alternative to gauge data for bias-correction in ungauged areas. However, GCM outputs corrected by ERA5 tend to overestimate overall precipitation and streamflow (by up to about 25% to 30%), while the correction results of CHIRPS significantly overestimate certain extreme indices (by up to about 50% to 100%). Based on the revealed performance of QPEs in correcting GCM outputs, this study provides references for selecting QPEs in GCM-based water resource projections in remote and ungauged areas.

Future Projections of Precipitation and Temperature in Northeast, Thailand using Bias-Corrected Global Climate Models

The increase in anthropogenic greenhouse gases (GHGs) has resulted in global climate change, with the Northeast region of Thailand experiencing the highest rate of change. This poses signifi cant risks of drought and its impact on crop yield production. To better understand the potential consequences of climate change and devise suitable adaptation strategies, this study aims to project precipitation and temperature data for Northeast Thailand from 2015 to 2055, using bias-corrected global climate models under Representative Concentration Pathways (RCPs) 4.5 and 8.5. The results showed that the annual precipitation and temperature increased from 2015 to 2055 under both RCPs. The annual average (minimum-maximum) of precipitation from the CanESM, GFDL, MIROC5, NorESM, and ensemble mean from four models under RCP4.5 were 1,238.83 (904.28-1,629.70), 1,227.82 (865.49- 1,721.31), 1,312.78 (876.60-1,616.38), 1,350.21 (985.55-1,625.00), and 1,282.41 (1,088.43-1,461.49) mm and RCP8.5 were 1,267.96 (864.24-1,712.86), 1,222.20 (863.79-1,835.43), 1,294.07 (843.04-1,752.41), 1,353.14 (1,059.50-1,827.23), and 1,284.34 (1,116.55-1,541.63) mm, respectively. While, those of temperature under RCP4.5 were 29.27 (28.15-30.35), 29.59 (27.98-31.20), 29.12 (27.65-30.70), 28.09 (26.96-29.14), and 29.02 (28.08-29.90) °C and RCP8.5 were 29.50 (28.12-31.40), 29.68 (28.23-31.50), 29.11 (27.51-30.26), 28.37 (27.11-29.88), and 29.17 (28.08-30.66) °C, respectively. These fi ndings suggest that the annual precipitation is lower under RCP4.5 compared to RCP8.5, while the temperature shows an increasing trend under both RCPs. Therefore, it is evident that climate change will manifest differently in Northeast Thailand, depending on local contexts and the measures implemented today. Understanding the impacts and risks of future climate change at a local scale and identifying adaptive solutions pose signifi cant challenges for the future.

Climate-altered Precipitation is more Important than Land Use when Modeling Ecosystem Services Associated with Surficial Processes.

Ecosystem services (ESs) associated with surficial processes may change according to shifts in land use, land cover, and climate parameters. Estimating these shifts can be important for land development planning, as urbanization alters soil processes that can manifest legacy effects. We employed the InVEST suite of models for sediment retention, nutrient delivery, and carbon storage to postulate how these ESs will change in the Upstate of South Carolina under future precipitation and land use and land cover (LULC) scenarios. We used the average precipitation from 1981-2010 and WorldClim precipitation projections for 2021-2040 and 2041-2060 to embody climatic precipitation shifts. For our LULC scenarios, we used 2011 and 2016 NLCD landscapes, then projected future LULC to hypothesize four future scenarios. We found that for the ES models that included both precipitation and LULC as inputs, precipitation dictated ES delivery far more heavily than land use or land cover. LULC scenarios produced consistent changes in ES delivery for all models except sediment export. Phosphorus and sediment exports increased between 2011 and 2016 due to LULC change, while nitrogen export stayed the same and carbon storage decreased. Land development that prioritizes forest cover will cause the least change in ESs, but allowing for continued forest loss to low-density development will have the most intense implications for ESs. Prioritization of land uses that preserve ESs associated with surficial processes will be critical to the longevity of agriculture and ecosystem integrity in this rapidly developing region. Land development planners should integrate consideration of ESs associated with surficial processes into future regional planning.

Projections of mean and extreme precipitation over China and their resolution dependence in the HighResMIP experiments

Projected near-future changes in mean and extreme precipitation by the end of the mid-21st century over China are investigated using the latest CMIP6 HighResMIP simulations grouped by different categories of horizontal and vertical resolutions. Evaluation of the historical simulations demonstrates improvements in the realism of the simulated precipitation over the different sub-regions with increased model resolutions. More apparent improvements are displayed by models with increased vertical resolution than those with increased horizontal resolutions. The near-future scenario of SSP5–8.5 suggests general increases in mean and extreme precipitation across Northwest and East-Northeast China. Also, models with coarser horizontal resolutions project an intensified precipitation seasonality over China. Models at relatively high vertical resolutions project decreased precipitation and enhanced droughts over Southwest China, while these are less apparent in models with relatively coarse vertical resolutions. All the simulations project a northward shift of summer and winter monsoon flows in East Asia. Meanwhile, a weaker summer monsoon across the tropics with a weakening of the monsoon trough over the South China Sea are projected. This indicates a less favorable environment for precipitation across the southern sub-regions. The complicated responses of precipitation to these environmental mechanisms are partly linked to the presented resolution-dependent uncertainties. Such uncertainties should be recognized and treated with caution in understanding the near-future hydrological changes in China.

Prioritizing the selection of CMIP6 model ensemble members for downscaling projections of CONUS temperature and precipitation

Given the mismatch between the large volume of data archived for the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and limited personnel and computational resources for downscaling, only a small fraction of the CMIP6 archive can be downscaled. In this work, we develop an approach to robustly sample projected hydroclimate states in CMIP6 for downscaling to test whether the selection of a single initial condition (IC) ensemble member from each CMIP6 model is sufficient to span the range of modeled hydroclimate over the conterminous United States (CONUS) and CONUS sub-regions. We calculate the pattern-centered root mean square difference of IC ensemble member anomalies relative to each model’s historical climatology for shared socioeconomic pathway (SSP) projections over 30-year time periods and compare the ratio of inter-model to intra-model variability for this metric. Regardless of SSP, inter-model variability is generally much greater than intra-model variability at the scales of the CONUS as a whole, as well as for most CONUS sub-regions. However for some variables and scenarios, inter- and intra-model variability are similar at sub-CONUS scales, indicating that selecting a single IC ensemble member per model may be sufficient to sample the range of projected hydroclimate states in the 21st Century across CONUS, but for specific regions and variables, more careful selection of ensemble members may be necessary. Regionally-resolved Taylor diagrams identify where more IC ensemble member downscaling efforts should be focused if resources are available to do so. Our results suggest that, with parsimonious sampling, the requisite computational expense of downscaling temperature and precipitation fields over the CONUS for subsequent CMIP activities may increase only marginally despite the great increase in data volumes with each successive CMIP phase.

Impact of Global Warming on U.S. Summertime Mesoscale Convective Systems: A Simple Lagrangian Parcel Model Perspective

Abstract Mesoscale convective systems (MCSs) are the dominant rainfall producer over the United States during the warm season, causing natural disasters and severe weather every year. Global climate models have large uncertainty in projecting precipitation changes in the future climate. Here, a simple Lagrangian parcel model is used to investigate the impact of global warming on MCS initiation and growth. The single-column parcel model projects a mean precipitation decrease over the central United States and an increase to its east, in agreement with the CMIP5 model projection. It also highlights the crucial role of current climate mean-state model bias in exaggerating the change in future mean precipitation projection by 25%. As for convective population, the model captures the decreased occurrence frequency of weak to moderate convection and increased frequency of strong convection due to the increased CAPE and CIN, in agreement with convection-permitting regional simulations. Novel parameterizations of gust-front propagation speed and subsidence strength are developed as guided by cloud-resolving simulations. The multicolumn parcel model employing those parameterizations captures readily the cold pool–induced upscale growth feature. It simulates smaller mesoscale clusters over the central United States under global warming due to gust-front slowdown and subsidence strength enhancement, which are further attributed to land aridity–induced weakening of initial accumulated precipitation and strengthening of updraft speed, respectively. That said, mesoscale clusters could become bigger under more favorable conditions in future climate, including boundary layer moistening, convection lifetime lengthening, and cold pool mechanical-lifting enhancement, which require further investigations to improve mechanistic understanding of future MCS changes.