UV-Indien Network- a network dedicated to the long-term monitoring of UV radiation in the Indian Ocean.

Radiation (UV) is one of the main components of solar radiation transmitted by the Earth's atmosphere. Exposure to UV radiation can have both positive and negative effects on the biosphere and humans in particular. Overexposure significantly increases the risk of skin cancer and eye problems.Ozone, cloud cover and zenithal solar angle are the main parameters affecting UV radiation levels at the surface. Stratospheric ozone in particular strongly absorbs UV radiation. A dense cloud cover absorbs UV radiation, while a split cloud cover may tend to amplify it.Although the stratospheric ozone layer is showing signs of recovery from reduced ozone-depleting substances. The impact of greenhouse gases on the climate is still in increase and global climate models anticipate an acceleration in Brewer-Dobson Circulation, which would lead to lower ozone levels in the tropics. Butler et al. (2016) estimate a decrease in stratospheric ozone in the tropics of 5 to 10 DU for all climate scenarios. Some recent projections (Lamy et al., 2019) predict a 2-3% increase in UVR in the southern tropical band, a region where UV levels are already extreme.The purpose of the UV-Indien network is to :- Monitor UV levels at different sites in the Western Indian Ocean (WIO)- Describe the annual and inter-annual variability of UV radiation in the WIO- Perform regional climate projections of UV radiations, validated by quality ground measurements.UV-Indien is split into three phases. The first phase began in 2016, with the deployment of the first measurement sites (Reunion Island, Madagascar, Seychelles, Rodrigues). These sites are equipped with a broadband radiometer measuring the UVI and a camera estimating the coverage and sometimes a spectrometer for the measurement of total ozone. The second phase from 2019, sees the extension of this network to 4 other sites (Juan de Nova, Diego Suarez, Fort Dauphin and Grande Comoros). The data validation phase began in 2019 (comparative study with satellite data) and will also propose the study of the variability of UV radiation on different sites. Finally, climate projections will be made from 2020 onwards and will use data from the network to validate the results.The aim of this communication is to describe the entire network and its objectives. The first results, as well as the first climatologies will also be discussed.

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The Impact of Different Absolute Solar Irradiance Values on Current Climate Model Simulations

Journal of Climate ◽

10.1175/jcli-d-13-00136.1 ◽

2014 ◽

Vol 27 (3) ◽

pp. 1100-1120 ◽

Cited By ~ 8

Author(s):

David H. Rind ◽

Judith L. Lean ◽

Jeffrey Jonas

Keyword(s):

Climate Change ◽

Solar Irradiance ◽

Cloud Cover ◽

Climate Models ◽

Climate Model ◽

Middle Atmosphere ◽

Global Climate ◽

Stratospheric Ozone ◽

Model Simulations ◽

The Impact

Abstract Simulations of the preindustrial and doubled CO2 climates are made with the GISS Global Climate Middle Atmosphere Model 3 using two different estimates of the absolute solar irradiance value: a higher value measured by solar radiometers in the 1990s and a lower value measured recently by the Solar Radiation and Climate Experiment. Each of the model simulations is adjusted to achieve global energy balance; without this adjustment the difference in irradiance produces a global temperature change of 0.4°C, comparable to the cooling estimated for the Maunder Minimum. The results indicate that by altering cloud cover the model properly compensates for the different absolute solar irradiance values on a global level when simulating both preindustrial and doubled CO2 climates. On a regional level, the preindustrial climate simulations and the patterns of change with doubled CO2 concentrations are again remarkably similar, but there are some differences. Using a higher absolute solar irradiance value and the requisite cloud cover affects the model’s depictions of high-latitude surface air temperature, sea level pressure, and stratospheric ozone, as well as tropical precipitation. In the climate change experiments it leads to an underestimation of North Atlantic warming, reduced precipitation in the tropical western Pacific, and smaller total ozone growth at high northern latitudes. Although significant, these differences are typically modest compared with the magnitude of the regional changes expected for doubled greenhouse gas concentrations. Nevertheless, the model simulations demonstrate that achieving the highest possible fidelity when simulating regional climate change requires that climate models use as input the most accurate (lower) solar irradiance value.

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Estimating the potential impact of climate change on sunflower yield in the Konya province of Turkey

The Journal of Agricultural Science ◽

10.1017/s0021859621000101 ◽

2021 ◽

pp. 1-13

Author(s):

Hudaverdi Gurkan ◽

Vakhtang Shelia ◽

Nilgun Bayraktar ◽

Y. Ersoy Yildirim ◽

Nebi Yesilekin ◽

...

Keyword(s):

Climate Change ◽

Climate Models ◽

Global Climate ◽

Model Performance ◽

Global Climate Models ◽

Climate Projections ◽

Climate Change Scenarios ◽

Adaptation To Climate Change ◽

Impact Of Climate Change ◽

The Impact

Abstract The impact of climate change on agricultural productivity is difficult to assess. However, determining the possible effects of climate change is an absolute necessity for planning by decision-makers. The aim of the study was the evaluation of the CSM-CROPGRO-Sunflower model of DSSAT4.7 and the assessment of impact of climate change on sunflower yield under future climate projections. For this purpose, a 2-year sunflower field experiment was conducted under semi-arid conditions in the Konya province of Turkey. Rainfed and irrigated treatments were used for model analysis. For the assessment of impact of climate change, three global climate models and two representative concentration pathways, i.e. 4.5 and 8.5 were selected. The evaluation of the model showed that the model was able to simulate yield reasonably well, with normalized root mean square error of 1.3% for the irrigated treatment and 17.7% for the rainfed treatment, a d-index of 0.98 and a modelling efficiency of 0.93 for the overall model performance. For the climate change scenarios, the model predicted that yield will decrease in a range of 2.9–39.6% under rainfed conditions and will increase in a range of 7.4–38.5% under irrigated conditions. Results suggest that temperature increases due to climate change will cause a shortening of plant growth cycles. Projection results also confirmed that increasing temperatures due to climate change will cause an increase in sunflower water requirements in the future. Thus, the results reveal the necessity to apply adequate water management strategies for adaptation to climate change for sunflower production.

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The impact of precipitation evaporation on the atmospheric aerosol distribution in EC-Earth v3.2.0

Geoscientific Model Development ◽

10.5194/gmd-11-1443-2018 ◽

2018 ◽

Vol 11 (4) ◽

pp. 1443-1465 ◽

Cited By ~ 3

Author(s):

Marco de Bruine ◽

Maarten Krol ◽

Twan van Noije ◽

Philippe Le Sager ◽

Thomas Röckmann

Keyword(s):

General Circulation ◽

Large Scale ◽

Climate Models ◽

Circulation Model ◽

Global Climate Models ◽

Orthogonal Polarization ◽

Earth Model ◽

Climate Projections ◽

Aerosol Distribution ◽

The Impact

Abstract. The representation of aerosol–cloud interaction in global climate models (GCMs) remains a large source of uncertainty in climate projections. Due to its complexity, precipitation evaporation is either ignored or taken into account in a simplified manner in GCMs. This research explores various ways to treat aerosol resuspension and determines the possible impact of precipitation evaporation and subsequent aerosol resuspension on global aerosol burdens and distribution. The representation of aerosol wet deposition by large-scale precipitation in the EC-Earth model has been improved by utilising additional precipitation-related 3-D fields from the dynamical core, the Integrated Forecasting System (IFS) general circulation model, in the chemistry and aerosol module Tracer Model, version 5 (TM5). A simple approach of scaling aerosol release with evaporated precipitation fraction leads to an increase in the global aerosol burden (+7.8 to +15 % for different aerosol species). However, when taking into account the different sizes and evaporation rate of raindrops following Gong et al. (2006), the release of aerosols is strongly reduced, and the total aerosol burden decreases by −3.0 to −8.5 %. Moreover, inclusion of cloud processing based on observations by Mitra et al. (1992) transforms scavenged small aerosol to coarse particles, which enhances removal by sedimentation and hence leads to a −10 to −11 % lower aerosol burden. Finally, when these two effects are combined, the global aerosol burden decreases by −11 to −19 %. Compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations, aerosol optical depth (AOD) is generally underestimated in most parts of the world in all configurations of the TM5 model and although the representation is now physically more realistic, global AOD shows no large improvements in spatial patterns. Similarly, the agreement of the vertical profile with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) satellite measurements does not improve significantly. We show, however, that aerosol resuspension has a considerable impact on the modelled aerosol distribution and needs to be taken into account.

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The impact of precipitation evaporation on the atmospheric aerosol distribution in EC-Earth v3.2.0

10.5194/gmd-2017-259 ◽

2017 ◽

Author(s):

Marco de Bruine ◽

Maarten Krol ◽

Twan van Noije ◽

Philippe Le Sager ◽

Thomas Röckmann

Keyword(s):

Large Scale ◽

Climate Models ◽

Global Climate ◽

Global Climate Models ◽

Earth Model ◽

Climate Projections ◽

Cloud Processing ◽

Aerosol Distribution ◽

Considerable Impact ◽

The Impact

Abstract. The representation of aerosol-cloud interaction in global climate models (GCMs) remains a large source of uncertainty in climate projections. Due to its complexity, precipitation evaporation is either ignored or taken into account in a simplified manner in GCMs. This research explores various ways to treat aerosol resuspension and determines the possible impact of precipitation evaporation and subsequent aerosol resuspension on global aerosol burdens and distribution. The representation of wet deposition of aerosols by large-scale precipitation in the EC-Earth model has been improved by utilising additional precipitation related 3-D fields from the dynamical core IFS in the chemistry and aerosol module TM5. A simple approach of scaling aerosol release with evaporated precipitation fraction leads to an increase in the global aerosol burden (+7.8 to +15 %, for different aerosol species). However, when taking into account the different sizes and evaporation rate of raindrops following Gong et al. (2006), the release of aerosols is strongly reduced, and the total aerosol burden decreases by −3.0 to −8.5 %. Moreover, inclusion of cloud processing based on observations by Mitra et al. (1992) transforms scavenged small aerosol to coarse particles, which enhances removal by sedimentation and hence leads to a lower burden of small size aerosol by −10 to −11 %. Finally, when these two effects are combined the global aerosol burden decreases by −11 to −19 %. Compared to MODIS satellite observations, AOD is generally underestimated in most parts of the world in all model set-ups and although the representation is now physically more realistic, global AOD shows no large improvements in spatial patterns. Similarly, the agreement of the vertical profile with CALIOP satellite measurements does not improve significantly. However, aerosol resuspension after precipitation evaporation has a considerable impact on the modelled aerosol distribution and needs to be taken into account.

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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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Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part II: Climate Change Impact Assessment)

Water ◽

10.3390/w13111548 ◽

2021 ◽

Vol 13 (11) ◽

pp. 1548

Author(s):

Suresh Marahatta ◽

Deepak Aryal ◽

Laxmi Prasad Devkota ◽

Utsav Bhattarai ◽

Dibesh Shrestha

Keyword(s):

Climate Change ◽

River Basin ◽

Climate Models ◽

Assessment Tool ◽

Swat Model ◽

Global Climate Models ◽

Climate Data ◽

The Future ◽

The Impact ◽

Mountainous River

This study aims at analysing the impact of climate change (CC) on the river hydrology of a complex mountainous river basin—the Budhigandaki River Basin (BRB)—using the Soil and Water Assessment Tool (SWAT) hydrological model that was calibrated and validated in Part I of this research. A relatively new approach of selecting global climate models (GCMs) for each of the two selected RCPs, 4.5 (stabilization scenario) and 8.5 (high emission scenario), representing four extreme cases (warm-wet, cold-wet, warm-dry, and cold-dry conditions), was applied. Future climate data was bias corrected using a quantile mapping method. The bias-corrected GCM data were forced into the SWAT model one at a time to simulate the future flows of BRB for three 30-year time windows: Immediate Future (2021–2050), Mid Future (2046–2075), and Far Future (2070–2099). The projected flows were compared with the corresponding monthly, seasonal, annual, and fractional differences of extreme flows of the simulated baseline period (1983–2012). The results showed that future long-term average annual flows are expected to increase in all climatic conditions for both RCPs compared to the baseline. The range of predicted changes in future monthly, seasonal, and annual flows shows high uncertainty. The comparative frequency analysis of the annual one-day-maximum and -minimum flows shows increased high flows and decreased low flows in the future. These results imply the necessity for design modifications in hydraulic structures as well as the preference of storage over run-of-river water resources development projects in the study basin from the perspective of climate resilience.

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Assessment of Arctic Cloud Cover Anomalies in Atmospheric Reanalysis Products Using Satellite Data

Journal of Climate ◽

10.1175/jcli-d-15-0861.1 ◽

2016 ◽

Vol 29 (17) ◽

pp. 6065-6083 ◽

Cited By ~ 25

Author(s):

Yinghui Liu ◽

Jeffrey R. Key

Keyword(s):

Cloud Cover ◽

Climate Models ◽

Global Climate ◽

Correlation Coefficients ◽

Cloud Amount ◽

Satellite Observation ◽

Skill Score ◽

Global Climate Models ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer

Abstract Cloud cover is one of the largest uncertainties in model predictions of the future Arctic climate. Previous studies have shown that cloud amounts in global climate models and atmospheric reanalyses vary widely and may have large biases. However, many climate studies are based on anomalies rather than absolute values, for which biases are less important. This study examines the performance of five atmospheric reanalysis products—ERA-Interim, MERRA, MERRA-2, NCEP R1, and NCEP R2—in depicting monthly mean Arctic cloud amount anomalies against Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations from 2000 to 2014 and against Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations from 2006 to 2014. All five reanalysis products exhibit biases in the mean cloud amount, especially in winter. The Gerrity skill score (GSS) and correlation analysis are used to quantify their performance in terms of interannual variations. Results show that ERA-Interim, MERRA, MERRA-2, and NCEP R2 perform similarly, with annual mean GSSs of 0.36/0.22, 0.31/0.24, 0.32/0.23, and 0.32/0.23 and annual mean correlation coefficients of 0.50/0.51, 0.43/0.54, 0.44/0.53, and 0.50/0.52 against MODIS/CALIPSO, indicating that the reanalysis datasets do exhibit some capability for depicting the monthly mean cloud amount anomalies. There are no significant differences in the overall performance of reanalysis products. They all perform best in July, August, and September and worst in November, December, and January. All reanalysis datasets have better performance over land than over ocean. This study identifies the magnitudes of errors in Arctic mean cloud amounts and anomalies and provides a useful tool for evaluating future improvements in the cloud schemes of reanalysis products.

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The representation of solar cycle signals in stratospheric ozone. Part II: Analysis of global models

10.5194/acp-2017-477 ◽

2017 ◽

Cited By ~ 3

Author(s):

Amanda C. Maycock ◽

Katja Matthes ◽

Susann Tegtmeier ◽

Hauke Schmidt ◽

Rémi Thiéblemont ◽

...

Keyword(s):

Solar Cycle ◽

Solar Irradiance ◽

Climate Models ◽

Climate Model ◽

Stratospheric Ozone ◽

Temperature Response ◽

Solar Variability ◽

High Latitudes ◽

Stratospheric Temperature ◽

The Impact

Abstract. The impact of changes in incoming solar irradiance on stratospheric ozone abundances should be included in climate model simulations to fully capture the atmospheric response to solar variability. This study presents the first systematic comparison of the solar-ozone response (SOR) during the 11 year solar cycle amongst different chemistry-climate models (CCMs) and ozone databases specified in climate models that do not include chemistry. We analyse the SOR in eight CCMs from the WCRP/SPARC Chemistry-Climate Model Initiative (CCMI-1) and compare these with three ozone databases: the Bodeker Scientific database, the SPARC/AC&C database for CMIP5, and the SPARC/CCMI database for CMIP6. The results reveal substantial differences in the representation of the SOR between the CMIP5 and CMIP6 ozone databases. The peak amplitude of theSOR in the upper stratosphere (1–5 hPa) decreases from 5 % to 2 % between the CMIP5 and CMIP6 databases. This difference is because the CMIP5 database was constructed from a regression model fit to satellite observations, whereas the CMIP6 database is constructed from CCM simulations, which use a spectral solar irradiance (SSI) dataset with relatively weak UV forcing. The SOR in the CMIP6 ozone database is therefore implicitly more similar to the SOR in the CCMI-1 models than to the CMIP5 ozone database, which shows a greater resemblance in amplitude and structure to the SOR in the Bodeker database. The latitudinal structure of the annual mean SOR in the CMIP6 ozone database and CCMI-1 models is considerably smoother than in the CMIP5 database, which shows strong gradients in the SOR across the midlatitudes owing to the paucity of observations at high latitudes. The SORs in the CMIP6 ozone database and in the CCMI-1 models show a strong seasonal dependence, including large meridional gradients at mid to high latitudes during winter; such seasonal variations in the SOR are not included in the CMIP5 ozone database. Sensitivity experiments with a global atmospheric model without chemistry (ECHAM6.3) are performed to assess the impact of changes in the representation of the SOR and SSI forcing between CMIP5 and CMIP6. The experiments show that the smaller amplitude of the SOR in the CMIP6 ozone database compared to CMIP5 causes a decrease in the modelled tropical stratospheric temperature response over the solar cycle of up to 0.6 K, or around 50 % of the total amplitude. The changes in the SOR explain most of the difference in the amplitude of the tropical stratospheric temperature response in the case with combined changes in SOR and SSI between CMIP5 and CMIP6. The results emphasise the importance of adequately representing the SOR in climate models to capture the impact of solar variability on the atmosphere. Since a number of limitations in the representation of the SOR in the CMIP5 ozone database have been identified, CMIP6 models without chemistry are encouraged to use the CMIP6 ozone database to capture the climate impacts of solar variability.

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What Do Global Climate Models Tell Us about Future Arctic Sea Ice Coverage Changes?

Climate ◽

10.3390/cli8010015 ◽

2020 ◽

Vol 8 (1) ◽

pp. 15 ◽

Cited By ~ 1

Author(s):

Ge Peng ◽

Jessica L. Matthews ◽

Muyin Wang ◽

Russell Vose ◽

Liqiang Sun

Keyword(s):

Sea Ice ◽

Climate Models ◽

Climate Adaptation ◽

Global Climate ◽

Arctic Sea Ice ◽

Global Climate Models ◽

Climate Projections ◽

Emission Scenarios ◽

Ice Coverage ◽

Arctic Sea

The prospect of an ice-free Arctic in our near future due to the rapid and accelerated Arctic sea ice decline has brought about the urgent need for reliable projections of the first ice-free Arctic summer year (FIASY). Together with up-to-date observations and characterizations of Arctic ice state, they are essential to business strategic planning, climate adaptation, and risk mitigation. In this study, the monthly Arctic sea ice extents from 12 global climate models are utilized to obtain projected FIASYs and their dependency on different emission scenarios, as well as to examine the nature of the ice retreat projections. The average value of model-projected FIASYs is 2054/2042, with a spread of 74/42 years for the medium/high emission scenarios, respectively. The earliest FIASY is projected to occur in year 2023, which may not be realistic, for both scenarios. The sensitivity of individual climate models to scenarios in projecting FIASYs is very model-dependent. The nature of model-projected Arctic sea ice coverage changes is shown to be primarily linear. FIASY values predicted by six commonly used statistical models that were curve-fitted with the first 30 years of climate projections (2006–2035), on other hand, show a preferred range of 2030–2040, with a distinct peak at 2034 for both scenarios, which is more comparable with those from previous studies.

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