Projections Of Future Change Research Articles

Evaluation of CMIP5 models and projected changes in temperatures over South Asia under global warming of 1.5 oC, 2 oC, and 3 oC

This study was designed to evaluate the spatiotemporal performance of the Coupled Model Intercomparison Project Phase 5 (CMIP5) models in the historical simulation and future projections of minimum (Tmin), maximum (Tmax), and mean temperature (Tmean) over South Asia (SA) during global warming of 1.5 oC, 2 oC, and 3 oC targets under RCP4.5 and RCP8.5 scenarios. It is worth mentioning that the present study is the first of its kind to use such a large number of CMIP5 models to project future changes in Tmin, Tmax, and Tmean over SA using three different warming thresholds. The results show that CSIRO–MK3–6–0, MIROC–ESM–CHEM, CNRM–CM5, CCSM4, and MRI–CGCM3 models relatively performed better with a consistent and accurate spatiotemporal simulation of Tmin, Tmax, and Tmean over SA. In terms of projected changes, Tmin, Tmax, and Tmean show a dominating and consistent warming pattern over SA with stronger intensity in higher latitude than mid–low latitudes under 1.5 oC, 2 oC, and 3 oC warming thresholds. The northwestern (eastern) regions of SA will witness greater (least) warming in Tmin, Tmax, and Tmean under all warming thresholds in RCP4.5 and RCP8.5 scenarios. Furthermore, the central and southern parts of SA will experience a moderate increase in Tmin, Tmax, and Tmean under all warming targets. The uneven and intensified patterns of Tmin, Tmax, and Tmean may result in temperature extremes, which would pose potential risks to the local population. Therefore, more attention should be paid on the regional and local perspectives to estimate the adverse impacts of these extremes under different global warming targets. We further suggest to project future changes in climate extremes over SA under different warming levels, which will be helpful in climate change adaptation and mitigation over the study region.

Changes in Extreme Precipitation across 30 Global River Basins

Extreme precipitation can cause disasters such as floods, landslides and crop destruction. A further study on extreme precipitation is essential for enabling reliable projections of future changes. In this study, the trends and frequency distribution changes in extreme precipitation across different major river basins around the world during 1960–2011 were examined based on two of the latest observational data sets respectively collected from 110,000 and 26,592 global meteorological stations. The results showed that approximately a quarter of basins have experienced statistically significant increase in maximum consecutive one-day, three-day and five-day precipitation (RX1day, RX3day and RX5day, respectively). In particular, dramatic increases were found in the recent decade for the Syr Darya River basin (SDR) and Amu Darya River basin (ADR) in the Middle East, while a decrease in RX3day and RX5day were seen over the Amur River basin in East Asia. One third of basins showed remarkable changes in frequency distributions of the three indices, and in most cases the distributions shifted toward larger amounts of extreme precipitation. Relative to the subperiod of 1960–1984, wider range of the three indices over SDR and ADR were detected for 1985–2011, indicating intensification along with larger fluctuations of extreme precipitation. However, some basins have frequency distributions shifting toward smaller amounts of RX3day and RX5day, such as the Columbia River basin and the Yellow River basin. The study has potential to provide the most up-to-date and comprehensive global picture of extreme precipitation, which help guide wiser public policies in future to mitigate the effects of these changes across global river basins.

Analysis and Projection of Land-Use/Land-Cover Dynamics through Scenario-Based Simulations Using the CA-Markov Model: A Case Study in Guanting Reservoir Basin, China

Understanding the rate and process of land-use/land-cover (LULC) change in a watershed is essential for managing natural resources and achieving sustainable development. Therefore, this study aims to analyze historical LULC change from 1980 to 2010 and project future changes in 2030, 2060, and 2090 in the Guanting Reservoir Basin (GRB), China, a critical water-supplying watershed for China’s capital Beijing, through scenario-based simulations. Two LULC scenarios, ‘business-as-usual’ and ‘governance’ (Gov), were projected using the Cellular Automata-Markov (CA–Markov) model. Historical LULC trend analysis shows that built-up land increased from 2.6% in 1980 to 5.26% in 2010, while cropland, grassland, and water body decreased. LULC conversion analysis indicates that, in general, grassland, cropland, and woodland were converted to built-up area from 1980 to 2010. The BAU scenario projects a dramatic increase in built-up area, rising from 2296.98 km2 (5.26%) in 2010 to 11,757.35 km2 (26.93%) in 2090 at the expense of cropland and grassland areas. Conversely, the Gov scenario predicts an increase in water body, woodland, and grassland, encouraging sustainable development. Overall, these results provide useful inputs to the LULC planners and water resources managers to elaborate on eco-friendly policies and regulations for GRB.

Subhourly rainfall in a convection-permitting model

Convection-permitting models (CPMs)—the newest generation of high-resolution climate models—have been shown to greatly improve the representation of subdaily and hourly precipitation, in particular for extreme rainfall. Intense precipitation events, however, often occur on subhourly timescales. The distribution of subhourly precipitation, extreme or otherwise, during a rain event can furthermore have important knock-on effects on hydrological processes. Little is known about how well CPMs represent precipitation at the subhourly timescale, compared to the hourly. Here we perform multi-decadal CPM simulations centred over Catalonia and, comparing with a high temporal-resolution gauge network, find that the CPM simulates subhourly precipitation at least as well as hourly precipitation is simulated. While the CPM inherits a dry bias found in its parent model, across a range of diagnostics and aggregation times (5, 15, 30 and 60 min) we find no consistent evidence that the CPM precipitation bias worsens with shortening temporal aggregation. We furthermore show that the CPM excels in its representation of subhourly extremes, extending previous findings at the hourly timescale. Our findings support the use of CPMs for modelling subhourly rainfall and add confidence to CPM-based climate projections of future changes in subhourly precipitation, particularly for extremes.

A Boundary Forcing Sensitivity Analysis of the West African Monsoon Simulated by the Modèle Atmosphérique Régional

The rainfall regime of West Africa is highly variable over a large range of space and time scales. With rainfall agriculture being predominent in the region, the local population is extremely vulnerable to intraseasonal dry spells and multi-year droughts as well as to intense rainfall over small time steps. Were this variability to increase, it might render the area close from becoming unhabitable. Anticipating any change is thus crucial from both a societal and a scientific perspective. Despite continuous efforts in Global Climate Model (GCM) development, there is still no agreement on the sign of the future rainfall regime change in the region. Regional Climate Models (RCMs) are used for more accurate projections of future changes as well as end-user-oriented impact studies. In this study, the sensitivity of the Modèle Atmosphérique Régional (MAR) to homogeneous perturbations in boundary forcing air temperature and/or SST is assessed with the aim to better understand (i) the thermodynamical imprint of the recent rainfall regime changes and (ii) the impact of errors in driving data on the West African rainfall regime simulated by an RCM. After an evaluation step where the model is proved to satisfactorily simulate the West African Monsoon (WAM), sensitivity experiments display contrasted, sizable and robust responses of the simulated rainfall regime. The rainfall responses to the boundary forcing perturbations compare in magnitude with the intrinsic model bias, giving support for such an analysis. A physical interpretation of the rainfall anomalies provides confidence in the model response consistency and shows the potential of such an experimental protocol for future climate change downscalling over this region.

Twenty-First Century Streamflow and Climate Change in Forest Catchments of the Central Appalachian Mountains Region, US

Forested catchments are critical sources of freshwater used by society, but anthropogenic climate change can alter the amount of precipitation partitioned into streamflow and evapotranspiration, threatening their role as reliable fresh water sources. One such region in the eastern US is the heavily forested central Appalachian Mountains region that provides fresh water to local and downstream metropolitan areas. Despite the hydrological importance of this region, the sensitivity of forested catchments to climate change and the implications for long-term water balance partitioning are largely unknown. We used long-term historic (1950–2004) and future (2005–2099) ensemble climate and water balance data and a simple energy–water balance model to quantify streamflow sensitivity and project future streamflow changes for 29 forested catchments under two future Relative Concentration Pathways. We found that streamflow is expected to increase under the low-emission pathway and decrease under the high-emission pathway. Furthermore, despite the greater sensitivity of streamflow to precipitation, larger increases in atmospheric demand offset increases in precipitation-induced streamflow, resulting in moderate changes in long-term water availability in the future. Catchment-scale results are summarized across basins and the region to provide water managers and decision makers with information about climate change at scales relevant to decision making.

Holocene atmospheric iodine evolution over the North Atlantic

Abstract. Atmospheric iodine chemistry has a large influence on the oxidizing capacity and associated radiative impacts in the troposphere. However, information on the evolution of past atmospheric iodine levels is restricted to the industrial period while its long-term natural variability remains unknown. The current levels of iodine in the atmosphere are controlled by anthropogenic ozone deposition to the ocean surface. Here, using high-resolution geochemical measurements from coastal eastern Greenland ReCAP (REnland ice CAP project) ice core, we report the first record of atmospheric iodine variability in the North Atlantic during the Holocene (i.e., the last 11 700 years). Surprisingly, our results reveal that the highest iodine concentrations in the record were found during the Holocene Thermal Maximum (HTM; ∼ 11 500–5500 years before-present). These high iodine levels could be driven by marine primary productivity resulting in an Early Holocene “biological iodine explosion”. The high and stable iodine levels during this past warm period are a useful observational constraint on projections of future changes in Arctic atmospheric composition and climate resulting from global warming.

Simulated CO2-induced ocean acidification for ocean in the East China: historical conditions since preindustrial time and future scenarios

Since preindustrial times, as atmospheric CO2 concentration increases, the ocean continuously absorbs anthropogenic CO2, reducing seawater pH and [{{rm{C}}{rm{O}}}_{3}^{2-}], which is termed ocean acidification. We perform Earth system model simulations to assess CO2-induced acidification for ocean in the East China, one of the most vulnerable areas to ocean acidification. By year 2017, ocean surface pH in the East China drops from the preindustrial level of 8.20 to 8.06, corresponding to a 35% rise in [H+], and reduction rate of pH becomes faster in the last two decades. Changes in surface seawater acidity largely result from CO2-induced changes in surface dissolved inorganic carbon (DIC), alkalinity (ALK), salinity and temperature, among which DIC plays the most important role. By year 2300, simulated reduction in sea surface [{{rm{C}}{rm{O}}}_{3}^{2-}] is 13% under RCP2.6, contrasted to 72% under RCP8.5. Furthermore, simulated results show that CO2-induced warming acts to mitigate reductions in [{{rm{C}}{rm{O}}}_{3}^{2-}], but the individual effect of oceanic CO2 uptake is much greater than the effect of CO2-induced warming on ocean acidification. Our study quantifies ocean acidification induced by anthropogenic CO2, and indicates the potentially important role of accelerated CO2 emissions in projections of future changes in biogeochemistry and ecosystem of ocean in the East China.

Spatiotemporal changes in the bud-burst date of herbaceous plants in Inner Mongolia grassland

Phenological modeling is not only important for the projection of future changes of certain phenophases but also crucial for systematically studying the spatiotemporal patterns of plant phenology. Based on ground phenological observations, we used two existing tem-perature-based models and 12 modified models with consideration of precipitation or soil moisture to simulate the bud-burst date (BBD) of four common herbaceous plants—Xanthium sibiricum, Plantago asiatica, Iris lactea and Taraxacum mongolicum—in temperate grasslands in Inner Mongolia. The results showed that (1) increase in temperature promoted the BBD of all species. However, effects of precipitation and soil moisture on BBD varied among species. (2) The modified models predicted the BBD of herbaceous plants with R2 ranging from 0.17 to 0.41 and RMSE ranging from 9.03 to 11.97 days, better than classical thermal models. (3) The spatiotemporal pattern of BBD during 1980–2015 showed that species with later BBD, e.g. X. sibiricum (mean: day of year 135.30) exhibited an evidently larger spatial difference in BBD (standard deviation: 13.88 days) than the other species. Our findings suggest that influences of temperature and water conditions need to be considered simultaneously in predicting the phenological response of herbaceous plants to climate change.

Preface

The 2019 6th International Conference on Coastal and Ocean Engineering (ICCOE 2019) was held in Bangkok, Thailand, April 25-28, 2019. ICCOE 2019 is dedicated to issues related to Coastal and Ocean Engineering.The objective of the conference is to bring together interested academics and industry experts in the field of Coastal and Ocean Engineering to a common forum. Three keynote speeches were delivered. Prof. Chou Loke Ming from Tropical Marine Science Institute, National University of Singapore, presented a keynote speech “Coral Reef Restoration and Habitat Creation to Mitigate Urbanization and Climate Change Impacts”. Assoc. Prof. Baoping Cai from China University of Petroleum gave the keynote speech “Development of Subsea Systems for Offshore Oil and Gas Equipment, with Fault Diagnosis Issues”. Professor Koh Hock Lye from Sunway University, Malaysia gave the third keynote speech “Sustainability in Theory and in Practice: A Three-decade Reflection on Education for Sustainability”. In addition, an Invited Speech “Convection-permitting Projections of Future Changes in Hydroclimatic Characteristics” was given by Dr. Shuo Wang from Hong Kong Polytechnic University. Papers were presented in four sessions of the conference: 1) Marine Ecology and Environment, 2) Ocean Engineering and Physics, 3) Wastewater Treatment, and 4) Health and Renewable Energy Application. 29 presentations were given by participants from 10 countries.This proceedings present a selection from papers submitted to the conference. All papers were subjected to peer-review by conference committee members and international reviewers. The papers selected depended on their quality and their relevance to the conference. This volume presents the recent advances in Coastal and Ocean Engineering and related areas, such as beach erosion and sediment transport, marine ecology and environment, climate change and sea level rise, lowland development and reclamation, subsea pipeline, coastal infrastructure development, deep sea engineering, and ocean activities platform.We would like to thank all the authors who have contributed to this volume, and also the organizing committee, reviewers, speakers, chairpersons, sponsors and all the conference participants for their support to ICCOE 2019.Prof. Chou Loke MingTropical Marine Science Institute, National University of Singapore, SingaporeMay 5, 2019

Copula‐Based Convection‐Permitting Projections of Future Changes in Multivariate Drought Characteristics

AbstractProbabilistic projections of future drought characteristics play a crucial role in climate change adaptation and disaster risk reduction. This study presents a copula‐based probabilistic framework for projecting future changes in multivariable drought characteristics through convection‐permitting Weather Research and Forecasting simulations with 4‐km horizontal grid spacing. A probabilistic multivariate drought index is introduced to examine the joint effects of drought indicators with uncertainty intervals for four major river basins located in South Central Texas of the United States. Markov chain Monte Carlo is used to address uncertainties in assessing copula parameters and in predicting climate‐induced changes in hydrological regimes. Our findings reveal that the severity and intensity of drought episodes can be amplified when considering the compound effects of soil moisture and runoff regimes by using the probabilistic multivariate drought index. The South Central Texas region is projected to experience more drought events with shorter duration and higher intensity in a changing climate. The drought severity will not necessarily increase due to the decreasing drought duration. In addition, our findings indicate that the intensity of future droughts is expected to increase as a result of the deficiency of soil moisture even though precipitation extremes are projected to become more frequent. Moreover, climate change impacts on multivariate drought characteristics will intensify with the increasing temporal scales (i.e., short‐, medium‐, and long‐term droughts) although the number of future drought events may decrease by the end of this century.

Quantifying the range of future glacier mass change projections caused by differences among observed past-climate datasets

Observed past climate data used as input in glacier models are expected to differ among datasets, particularly those for precipitation at high elevations. Differences among observed past climate datasets have not yet been described as a cause of uncertainty in projections of future changes in glacier mass, although uncertainty caused by varying future climate projections among general circulation models (GCMs) has often been discussed. Differences among observed past climate datasets are expected to propagate as uncertainty in future changes in glacier mass due to bias correction of GCMs and calibration of glacier models. We project ensemble future changes in the mass of glaciers in Asia through the year 2100 using a glacier model. A set of 18 combinations of inputs, including two observed past air temperature datasets, three observed past precipitation datasets, and future air temperature and precipitation projections from three GCMs were used. The uncertainty in projected changes in glacier mass was partitioned into three distinct sources: GCM uncertainty, observed past air temperature uncertainty, and observed past-precipitation uncertainty. Our findings indicate that, in addition to the differences in climate projections among GCMs, differences among observed past climate datasets propagate fractional uncertainties of about 15% into projected changes in glacier mass. The fractional uncertainty associated with observed past precipitation was 33–50% that of the observed air temperature. Differences in observed past air temperatures and precipitation did not propagate equally into the ultimate uncertainty of glacier mass projection when ablation was dominant.

Valuing Blue Carbon Changes in the Arctic Ocean

The ocean capacity to store carbon is crucial, and currently absorbs about 25% CO2 supply to the atmosphere. The ability to store carbon has an economic value, but such estimates are not common for ocean environments, and not yet estimated for the Arctic Ocean. With the severe climatic changes in the Arctic Ocean, impacting sea ice and potentially the vertical carbon transport mechanisms, a projection of future changes in Arctic Ocean carbon storage is also of interest. In order to value present and evolving carbon storage in the changing Arctic marine environment we combine an ocean model with an economic analysis. Placing a value on these changes helps articulate the importance of the carbon storage service to society. The standing stock and fluxes of organic and inorganic carbon from the atmosphere, rivers, shelves and through the gateways linking to lower latitudes, and to the deep of the Arctic Ocean are investigated using the physically-chemically-biologically coupled SINMOD model. To obtain indications of the effect of climate change, trajectories of two IPCC climate scenarios RCP 4.5 and RCP 8.5 from the Max Planck Institute were used for the period 2006-2099. The results show an increase in the net carbon storage in the Arctic Ocean in this time period to be 1.0% and 2.3% in the RCP 4.5 and RCP 8.5 scenarios, respectively. Most of this increase is caused by an increased atmospheric CO2 uptake until 2070. The continued increase in inorganic carbon storage between 2070 and 2099 results from increased horizontal influx from lower latitude marine regions. First estimates of carbon storage values in the Arctic Ocean are calculated using the social cost of carbon and carbon market values as two outer bounds from 2019 to 2099, based on the simulated scenarios. We find the Arctic Ocean will over the time period studied increase its storage of carbon to a value of between €27.6 billion and €1 trillion. This paper clearly neglects a multitude of different negative consequences of climate change in the Arctic, but points to the fact that there are also some positive counterbalancing effects.

Effects of Bias-Correcting Climate Model Data on the Projection of Future Changes in High Flows

Bias-correction methods are commonly applied to climate model data in hydrological climate impact studies. This is due to the often large deviations between simulated and observed climate variables. These biases may cause unrealistic simulation results when directly using the climate model data as input for hydrological models. Our analysis of the EURO-CORDEX (Coordinated Downscaling Experiment for Europe) data for the Northwestern part of Germany showed substantial biases for all climatological input variables needed by the hydrological model PANTA RHEI. The sensitivity for climatological input data demonstrated that changes in only one climate variable significantly affect the simulated average discharge and mean annual peak flow. The application of bias correction methods of different complexity on the climate model data improved the plausibility of hydrological modeling results for the historical period 1971–2000. The projections for the future period 2069–2099 for high flows indicate on average small changes for representative concentration pathway (RCP) 4.5 and an increase of approximately 10% for RCP8.5 when applying non-bias corrected climate model data. These values significantly differed when applying bias correction. The bias correction methods were evaluated in terms of their ability to (a) maintain the change signal for precipitation and (b) the goodness of fit for hydrological parameters for the historical period. Our results for this evaluation indicated that no bias correction method can explicitly be preferred over the others.

Annihilation of the Somali upwelling system during summer monsoon

Somali upwelling system during northern summer is believed to be the largest upwelling region in the Indian Ocean and has motivated some of the early studies on the Indian Ocean. Here we present results from observations and ocean model to show that the upwelling along the Somali coast is limited to the early phase of the summer monsoon and later primarily limited to the eddy dominated flows in the northern and some extent in the southern part of the coast. Major part of the Somali coast (~60% of the entire coastal length) shows prominent downwelling features driven by offshore negative windstress curl and subsurface entrainment mixing. Further, we show that the surface cooling of coastal waters are dominantly driven by subsurface entrainment and surface heat fluxes. These findings not only augment the existing knowledge of the Somali upwelling system, but also have serious implications on the regional climate. Most importantly, our analysis underscores the use of alongshore winds only to project future (climate driven) changes in the upwelling intensity along this coast.

A two-stage bias correction approach for downscaling and projection of daily average temperature

Reliable projection of climate is essential for climate change impact assessment and mitigation planning. General Circulation Models (GCMs) simulations are generally downscaled into much finer spatial resolution for climate change impact studies at local and regional scales. The objective of the present study is to use a two-stage bias correction approach for downscale and project future changes of daily average temperature. The approach was applied for downscaling and projection of daily average temperature of Senai meteorological station located in Johor Bahru, Malaysia using a GCM of Coupled Model Intercomparison Project Phase 5 (CMIP5) under four representative concentration pathways (RCP) scenarios. The two-stage bias correction method was based on correction in mean factor and variability inflation factor. The model performances were assessed using different statistical measures including mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), index of agreement (MD), Nash–Sutcliffe model efficiency (NSE) and coefficient of determination (R2). Results showed that the downscaling method could simulate historical daily average temperature at the station very well. The GCM projected an increase in daily average temperature by 1.4ºC, 2.2ºC, 2.5ºC, and 3.4ºC under RCP2.6, RCP4.5, RCP6.0 and RCP8.5 scenarios, respectively in the end of this century. It is expected that the finding of the study would help in climate change impact assessment and adopting necessary adaptation measures.

A synoptic assessment of the summer extreme rainfall over the middle reaches of Yangtze River in CMIP5 models

The summer mean and extreme rainfall over the middle reaches of Yangtze River (MRYR) are underestimated in many models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Our earlier work has identified three synoptic-scale circulation patterns associated with extreme rainfall events over the MRYR during early summer. It is verified here that the presence of these patterns indeed increases the likelihood of the occurrence of extreme rainfall over the MRYR. This relationship between the synoptic-scale circulation pattern and extreme rainfall is reproduced by only a subset of CMIP5 models, where the underestimated frequency of the corresponding synoptic-scale circulation patterns partly explains the underestimated frequency of extreme rainfall thus the summer total over the MRYR. Our analysis also reveals that a few models could “accidentally” simulate a realistic rainfall total and probability distribution of daily rain rate over the MRYR region during summer by generating their own model-dependent synoptic-scale circulation patterns that are not typically seen in observations. These findings suggest that a projection of future changes in extreme rainfall over the MRYR will be better constrained dynamically if we use a subset of models that can reproduce the “rainfall-circulation” relationship, given the diverse response of different circulation patterns to radiative forcing changes in the atmosphere. The results presented here also demonstrate the importance of tracking biases of synoptic-scale circulations in understanding model deficiencies in precipitation simulation, in addition to investigating problems in model physics such as cumulus schemes and microphysics parameterization. Nevertheless, these results are based on the analyses of extreme rainfall in one region and one season, further research covering other regions/seasons is needed.

Estimation of planting date in paddy fields by time-series MODIS data for basin-scale rice production modeling

Projection of future changes in agricultural water demand and supply through numerical model simulations can be one of the reference materials used for long-term rural planning under climate change. With a distributed hydrological model incorporating an agricultural sub-model, time-series rice growth and its yield can be simulated in correspondence with time-series soil moisture simulated by a hydrological model. Whereas such an agro-hydrological model is useful for assessing the climate change impact on agriculture in that it can simulate the potential rice production and the water-related damages on it against the given rainfall and meteorological condition, one of the challenges in simulating a realistic situation by a model is how to give a planting date in the model in regions where it is not fixed to a certain calendar date and differs in different fields and in different years. In this study, we examined an algorithm to estimate planting date in paddy fields using satellite data. The data we used are the 16-day composite Enhanced Vegetation Index (EVI) product by the moderate resolution imaging spectroradiometer (MODIS), which provides global coverage with 250-m spatial resolution. The time series of the EVI values at each pixel was analyzed first to detect heading dates as the local maxima in each year. Considering the number of days for growing rice, the existence of the second and the third rice cropping was judged from the time intervals of the local maxima. The planting dates were then estimated from the determined heading dates and local minima of the EVI time-series EVI. The estimated planting dates were validated against national statistical data in Japan and also with field-surveyed data in Cambodia. They showed reasonable estimations in the areal statistics, but there remained gaps in the comparison at individual fields.

Projections of future changes in solar radiation in China based on CMIP5 climate models

Surface solar radiation research is important for understanding future climate change and the application of large-scale photovoltaic systems. We used the coupled model intercomparison project phase 5 (CMIP5) under the RCP8.5 scenario to project potential changes in surface solar radiation, surface temperatures, and cloud fractions between 2006 and 2049 in China, as well as how these changes may affect photovoltaic power generation. The results show that the following. (1) For surface temperatures, the median trends of all considered models show warming in China of 0.05 K/year. The maximum positive trends for all-sky radiation appear in the southeast of China, reaching 0.4 W/m2/year. Cloud cover exhibits a mainly decreasing trend in the overall region of China. (2) The all-sky radiation of most selected regions shows a decreasing trend. The maximum negative value (−0.08 W/m2/year) appears in Qinghai. (3) Compared with the average photovoltaic power output from 2006 to 2015, the photovoltaic power output in western China will decrease by −0.04 %/ year, while photovoltaic power output in southeastern China will increase by 0.06–0.1%/year.

Fidelity of the Observational/Reanalysis Datasets and Global Climate Models in Representation of Extreme Precipitation in East China

AbstractRealistic reproduction of historical extreme precipitation has been challenging for both reanalysis and global climate model (GCM) simulations. This work assessed the fidelities of the combined gridded observational datasets, reanalysis datasets, and GCMs [CMIP5 and the Chinese Academy of Sciences Flexible Global Ocean–Atmospheric Land System Model–Finite-Volume Atmospheric Model, version 2 (FGOALS-f2)] in representing extreme precipitation over East China. The assessment used 552 stations’ rain gauge data as ground truth and focused on the probability distribution function of daily precipitation and spatial structure of extreme precipitation days. The TRMM observation displays similar rainfall intensity–frequency distributions as the stations. However, three combined gridded observational datasets, four reanalysis datasets, and most of the CMIP5 models cannot capture extreme precipitation exceeding 150 mm day−1, and all underestimate extreme precipitation frequency. The observed spatial distribution of extreme precipitation exhibits two maximum centers, located over the lower-middle reach of Yangtze River basin and the deep South China region, respectively. Combined gridded observations and JRA-55 capture these two centers, but ERA-Interim, MERRA, and CFSR and almost all CMIP5 models fail to capture them. The percentage of extreme rainfall in the total rainfall amount is generally underestimated by 25%–75% in all CMIP5 models. Higher-resolution models tend to have better performance, and physical parameterization may be crucial for simulating correct extreme precipitation. The performances are significantly improved in the newly released FGOALS-f2 as a result of increased resolution and a more realistic simulation of moisture and heating profiles. This work pinpoints the common biases in the combined gridded observational datasets and reanalysis datasets and helps to improve models’ simulation of extreme precipitation, which is critically important for reliable projection of future changes in extreme precipitation.