Numerical simulation of surface solar radiation over Southern Africa. Part 1: Evaluation of regional and global climate models

Quantifying trends in surface solar radiation (SSR) of unforced simulations is of substantial importance when one tries to quantify the anthropogenic effect in forced trends, as the net effect may be dampened or amplified by the internal variability of the system. In our analysis, we consider trends on different temporal scales (10, 30, 50 and 100 years) from 58 global climate models, participating in the Coupled Model Intercomparison Project - Phase 6 (CMIP6). We calculate the trends at the grid-box level for all-sky and clear-sky SSR using annual mean data of the multi-century pre-industrial control (piControl) experiments. The trends from both variables are found to depend strongly on the geographical region, as the most pronounced trends of the all-sky variable are observed in the Tropical Pacific, while the largest clear-sky trends are found in the large desert regions. Inspecting for each grid cell the statistical distribution of occurring N-year trends &#160;shows that they are normally distributed in the majority of grid cells for both all-sky and clear-sky SSR. The 75-th percentile taken from these distributions (i.e. a positive trend with a 25 % chance of occurrence) varies with geographical region, taking values in the ranges 0.79 - 12.03 Wm-2/decade for 10-year trends, 0.15 - 2.05 Wm-2/decade for 30-year trends, 0.07 - 0.92 Wm-2/decade for 50-year trends and 0.02 - 0.29 Wm-2/decade for 100-year trends for all-sky SSR. The unforced trends become less significant on longer timescales &#8211; the trend medians, corresponding to the above ranges, are 3.18 Wm-2/decade, 0.62 Wm-2/decade, 0.29 Wm-2/decade, 0.10 Wm-2/decade respectively. The trends for clear-sky SSR are found to differ from the all-sky SSR by a factor of 0.16 on average, independent of the trend length. The model spread becomes greater at longer trend timescales, the differences being more substantial between large model families rather than between individual models. To elucidate the dominant causes of variability in different regions, we examine the correlations of the SSR variables with ambient aerosol optical thickness at 550 nm, atmosphere mass content of water vapour, cloud area fraction and albedo.

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numerical simulations of Surface Solar Radiation over southern Africa for the past and future

10.5194/egusphere-egu21-3811 ◽

2021 ◽

Author(s):

Chao Tang ◽

Béatrice Morel ◽

Martin Wild ◽

Benjamin Pohl ◽

Babatunde Abiodun ◽

...

Keyword(s):

Climate Change ◽

Solar Radiation ◽

Southern Africa ◽

Climate Models ◽

Austral Summer ◽

Internal Variability ◽

Surface Solar Radiation ◽

Data Set ◽

The Past ◽

Impacts Of Climate Change

This study evaluates the possible impacts of climate change on Surface Solar Radiation (SSR), as a renewable energy resource, in Southern Africa (SA). Performance of climate models in reproducing the mean states and long-term trend of SSR are assessed by validating five Regional Climate Models (RCM) that participated in the Coordinated Regional Downscaling Experiment program over Africa (CORDEX-Africa) along with their ten driving General Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) over SA. Then the possible impacts of climate change on SSR are evaluated. The uncertainties in the GCM-RCM&#160;model&#160;chains have also been quantitatively estimated.Results show that in the past (1) GCMs overestimate SSR over SA in terms of their multi-model mean by about 1 W/m2 (compensation of opposite biases over sub-regions) and 7.5 W/m2 in austral summer and winter respectively compared to SARAH-2 (Surface Solar Radiation Data Set&#8212;Heliosat Edition 2); However, RCMs underestimate SSR in both seasons with Mean Bias Errors of about &#8722;30 W/m2in austral summer and about &#8722;14 W/m2 in winter. And the discrepancies in the simulated SSR are larger in the RCMs than in the GCMs. (2) In terms of trend during the &#8220;brightening&#8221; period 1990&#8211;2005, both GCMs and RCMs (driven by ERA-Interim and GCMs) simulate an SSR trend of less than 1 W/m2 per decade. However, variations of SSR trend exist among different references data. (3) For individual RCM models, their SSR bias fields seem rather insensitive with respect to the different lateral forcings provided by ERA-INTERIM and various GCMs, in line with previous findings over Europe.In future, (1) multi-model mean projections of SSR trends are consistent between the GCMs and their nested RCMs. Two areas with statistically significant SSR changes are found: over the center of SA, GCMs and RCMs project a statistically significant increase in SSR by 2099 of about +1.5 W/m2 per decade in RCP8.5 during the DJF season. Over Eastern Equatorial Africa a statistically significant decrease in SSR of about &#8722;2 W/m2 per decade in RCP8.5 is found in the ensemble means in DJF. (3) SSR projections are fairly similar between RCP8.5 and RCP4.5 before 2050 and then the differences between those two scenarios increase up to about 1 W/m2 per decade with larger changes in RCP8.5 than in RCP4.5 scenario. (4) These SSR evolutions are generally consistent with projected changes in Cloud Cover Fraction over SA and may also related to the changes in atmosphere water vapor content. (5) SSR change signals emerge earlier out of internal variability estimated from ERA-Interim in DJF in RCMs than in GCMs, which suggests a higher sensitivity of RCMs to the forcing RCP scenarios than their driving GCMs in simulating SSR changes. (6) The uncertainty in SSR change projections is likely dominated by the internal climate variability before 2050, and after that model and scenario uncertainties become as important as internal variability until the end of the 21st century.

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Applicability of CMIP6 models for building climate projections for northern Europe

10.35614/isbn.9789523361416 ◽

2021 ◽

Author(s):

Kimmo Ruosteenoja ◽

Keyword(s):

Solar Radiation ◽

Greenhouse Gas ◽

Climate Models ◽

Global Climate ◽

Global Climate Models ◽

Evaluation Procedure ◽

Climate Projections ◽

Climate Variables ◽

Mean Temperature ◽

Rms Errors

In this report, we have evaluated the performance of nearly 40 global climate models (GCMs) participating in Phase 6 of the Coupled Model Intercomparison Project (CMIP6). The focus is on the northern European area, but the ability to simulate southern European and global climate is discussed as well. Model evaluation was started with a technical control; completely unrealistic values in the GCM output files were identified by seeking the absolute minimum and maximum values. In this stage, one GCM was rejected totally, and furthermore individual output files from two other GCMs. In evaluating the remaining GCMs, the primary tool was the Model Climate Performance Index (MCPI) that combines RMS errors calculated for the different climate variables into one index. The index takes into account both the seasonal and spatial variations in climatological means. Here, MCPI was calculated for the period 1981—2010 by comparing GCM output with the ERA-Interim reanalyses. Climate variables explored in the evaluation were the surface air temperature, precipitation, sea level air pressure and incoming solar radiation at the surface. Besides MCPI, we studied RMS errors in the seasonal course of the spatial means by examining each climate variable separately. Furthermore, the evaluation procedure considered model performance in simulating past trends in the global-mean temperature, the compatibility of future responses to different greenhouse-gas scenarios and the number of available scenario runs. Daily minimum and maximum temperatures were likewise explored in a qualitative sense, but owing to the non-existence of data from multiple GCMs, these variables were not incorporated in the quantitative validation. Four of the 37 GCMs that had passed the initial technical check were regarded as wholly unusable for scenario calculations: in two GCMs the responses to the different greenhouse gas scenarios were contradictory and in two other GCMs data were missing from one of the four key climate variables. Moreover, to reduce inter-GCM dependencies, no more than two variants of any individual GCM were included; this led to an abandonment of one GCM. The remaining 32 GCMs were divided into three quality classes according to the assessed performance. The users of model data can utilize this grading to select a subset of GCMs to be used in elaborating climate projections for Finland or adjacent areas. Annual-mean temperature and precipitation projections for Finland proved to be nearly identical regardless of whether they were derived from the entire ensemble or by ignoring models that had obtained the lowest scores. Solar radiation projections were somewhat more sensitive.

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Multi-Decadal Variability and Future Changes in Precipitation over Southern Africa

Atmosphere ◽

10.3390/atmos12060742 ◽

2021 ◽

Vol 12 (6) ◽

pp. 742

Author(s):

Kenny Thiam Choy Lim Kam Sian ◽

Jianhong Wang ◽

Brian Odhiambo Ayugi ◽

Isaac Kwesi Nooni ◽

Victor Ongoma

Keyword(s):

Southern Africa ◽

Climate Models ◽

Decadal Variability ◽

Global Climate ◽

Spatial Scales ◽

Precipitation Amount ◽

High Elevation ◽

Global Climate Models ◽

Initial Increase ◽

Precipitation Trends

The future planning and management of water resources ought to be based on climate change projections at relevant temporal and spatial scales. This work uses the new regional demarcation for Southern Africa (SA) to investigate the spatio-temporal precipitation variability and trends of centennial-scale observation and modeled data, based on datasets from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The study employs several statistical methods to rank the models according to their precipitation simulation ability. The Theil–Sen slope estimator is used to assess precipitation trends, with a Student’s t-test for the significance test. The comparison of observation and model historical data enables identification of the best-performing global climate models (GCMs), which are then employed in the projection analysis under two Shared Socioeconomic Pathways (SSPs): SSP2-4.5 and SSP5-8.5. The GCMs adequately capture the annual precipitation variation but with a general overestimation, especially over high-elevation areas. Most of the models fail to capture precipitation over the Lesotho-Eswatini area. The three best-performing GCMs over SA are FGOALS-g3, MPI-ESM1-2-HR and NorESM2-LM. The sub-regions demonstrate that precipitation trends cannot be generalized and that localized studies can provide more accurate findings. Overall, precipitation in the wet and dry seasons shows an initial increase during the near future over western and eastern SA, followed by a reduction in precipitation during the mid- and far future under both projection scenarios. Madagascar is expected to experience a decrease in precipitation amount throughout the twenty-first century.

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Extreme Events in High Resolution CMCC Regional and Global Climate Models

SSRN Electronic Journal ◽

10.2139/ssrn.1960499 ◽

2011 ◽

Author(s):

Enrico Scoccimarro ◽

Silvio Gualdi ◽

Antonella Sanna ◽

Edoardo Bucchignani ◽

Myriam Montesarchio

Keyword(s):

High Resolution ◽

Extreme Events ◽

Climate Models ◽

Global Climate ◽

Global Climate Models

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TWO SECONDARY SCIENCE TEACHERS IMPLEMENTATION OF A CURRICULAR INTERVENTION WHEN TEACHING WITH GLOBAL CLIMATE MODELS

10.1130/abs/2019am-337015 ◽

2019 ◽

Author(s):

Kimberly Carroll Steward ◽

◽

Devarati Bhattacharya ◽

Cory Forbes ◽

Mark Chandler

Keyword(s):

Science Teachers ◽

Climate Models ◽

Global Climate ◽

Global Climate Models ◽

Secondary Science ◽

Curricular Intervention ◽

Secondary Science Teachers

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Global climatology and trends in convective environments from ERA5 and rawinsonde data

npj Climate and Atmospheric Science ◽

10.1038/s41612-021-00190-x ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Mateusz Taszarek ◽

John T. Allen ◽

Mattia Marchio ◽

Harold E. Brooks

Keyword(s):

Large Scale ◽

Climate Models ◽

Global Climate ◽

Convective Available Potential Energy ◽

Global Climate Models ◽

Convective Precipitation ◽

The Past ◽

The Tropics ◽

Rawinsonde Data

AbstractGlobally, thunderstorms are responsible for a significant fraction of rainfall, and in the mid-latitudes often produce extreme weather, including large hail, tornadoes and damaging winds. Despite this importance, how the global frequency of thunderstorms and their accompanying hazards has changed over the past 4 decades remains unclear. Large-scale diagnostics applied to global climate models have suggested that the frequency of thunderstorms and their intensity is likely to increase in the future. Here, we show that according to ERA5 convective available potential energy (CAPE) and convective precipitation (CP) have decreased over the tropics and subtropics with simultaneous increases in 0–6 km wind shear (BS06). Conversely, rawinsonde observations paint a different picture across the mid-latitudes with increasing CAPE and significant decreases to BS06. Differing trends and disagreement between ERA5 and rawinsondes observed over some regions suggest that results should be interpreted with caution, especially for CAPE and CP across tropics where uncertainty is the highest and reliable long-term rawinsonde observations are missing.

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Regions of intensification of extreme snowfall under future warming

Scientific Reports ◽

10.1038/s41598-021-95979-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Lennart Quante ◽

Sven N. Willner ◽

Robin Middelanis ◽

Anders Levermann

Keyword(s):

Climate Change ◽

Global Warming ◽

Observational Data ◽

Climate Models ◽

Hydrological Cycle ◽

Global Climate ◽

Global Climate Models ◽

Average Intensity ◽

High Latitudes ◽

Future Warming

AbstractDue to climate change the frequency and character of precipitation are changing as the hydrological cycle intensifies. With regards to snowfall, global warming has two opposing influences; increasing humidity enables intense snowfall, whereas higher temperatures decrease the likelihood of snowfall. Here we show an intensification of extreme snowfall across large areas of the Northern Hemisphere under future warming. This is robust across an ensemble of global climate models when they are bias-corrected with observational data. While mean daily snowfall decreases, both the 99th and the 99.9th percentiles of daily snowfall increase in many regions in the next decades, especially for Northern America and Asia. Additionally, the average intensity of snowfall events exceeding these percentiles as experienced historically increases in many regions. This is likely to pose a challenge to municipalities in mid to high latitudes. Overall, extreme snowfall events are likely to become an increasingly important impact of climate change in the next decades, even if they will become rarer, but not necessarily less intense, in the second half of the century.

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