scholarly journals Retrieving Decadal Climate Change from Satellite Radiance Observations—A 100-year CO2 Doubling OSSE Demonstration

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1247 ◽  
Author(s):  
William L. Smith ◽  
Elisabeth Weisz ◽  
Robert Knuteson ◽  
Henry Revercomb ◽  
Daniel Feldman
Keyword(s):  
Climate Change ◽  
Linear Regression ◽  
Climate Model ◽  
International System ◽  
Spectral Radiance ◽  
Retrieval Algorithm ◽  
Climate Variables ◽  
Infrared Radiance ◽  
Infrared Spectral ◽  
Cross Calibration

Preparing for climate change depends on the observation and prediction of decadal trends of the environmental variables, which have a direct impact on the sustainability of resources affecting the quality of life on our planet. The NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission is proposed to provide climate quality benchmark spectral radiance observations for the purpose of determining the decadal trends of climate variables, and validating and improving the long-range climate model forecasts needed to prepare for the changing climate of the Earth. The CLARREO will serve as an in-orbit, absolute, radiometric standard for the cross-calibration of hyperspectral radiance spectra observed by the international system of polar operational satellite sounding sensors. Here, we demonstrate that the resulting accurately cross-calibrated polar satellite global infrared spectral radiance trends (e.g., from the Metop IASI instrument considered here) can be interpreted in terms of temperature and water vapor profile trends. This demonstration is performed using atmospheric state data generated for a 100-year period from 2000–2099, produced by a numerical climate model prediction that was forced by the doubling of the global average atmospheric CO2 over the 100-year period. The vertical profiles and spatial distribution of temperature decadal trends were successfully diagnosed by applying a linear regression profile retrieval algorithm to the simulated hyperspectral radiance spectra for the 100-year period. These results indicate that it is possible to detect decadal trends in atmospheric climate variables from high accuracy all-sky satellite infrared radiance spectra using the linear regression retrieval technique.

scholarly journals Producing hydrologic scenarios from raw climate model outputs using an asynchronous modelling framework

2021 ◽  
Author(s):  
Simon Ricard ◽  
Philippe Lucas-Picher ◽  
François Anctil
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Water Discharge ◽  
Climate Change Impacts ◽  
Climate Variables ◽  
Post Processing ◽  
Hydrologic Models ◽  
Simulated Climate ◽  
Meteorological Observations ◽  
The Impact

Abstract. Statistical post-processing of climate model outputs is a common hydroclimatic modelling practice aiming to produce climate scenarios that better fit in-situ observations and to produce reliable stream flows forcing calibrated hydrologic models. Such practice is however criticized for disrupting the physical consistency between simulated climate variables and affecting the trends in climate change signals imbedded within raw climate simulations. It also requires abundant good-quality meteorological observations, which are not available for many regions in the world. A simplified hydroclimatic modelling workflow is proposed to quantify the impact of climate change on water discharge without resorting to meteorological observations, nor for statistical post-processing of climate model outputs, nor for calibrating hydrologic models. By combining asynchronous hydroclimatic modelling, an alternative framework designed to construct hydrologic scenarios without resorting to meteorological observations, and quantile perturbation applied to streamflow observations, the proposed workflow produces sound and plausible hydrologic scenarios considering: (1) they preserve trends and physical consistency between simulated climate variables, (2) are implemented from a modelling cascades despite observation scarcity, and (3) support the participation of end-users in producing and interpreting climate change impacts on water resources. The proposed modelling workflow is implemented over four subcatchments of the Chaudière River, Canada, using 9 North American CORDEX simulations and a pool of lumped conceptual hydrologic models. Forced with raw climate model outputs, hydrologic models are calibrated over the reference period according to a calibration metric designed to function with temporally uncorrelated observed and simulated streamflow values. Perturbation factors are defined by relating each simulated streamflow quantiles over both reference and future periods. Hydrologic scenarios are finally produced by applying perturbation factors to available streamflow observations.

scholarly journals Achieving Climate Change Absolute Accuracy in Orbit

2013 ◽  
Vol 94 (10) ◽  
pp. 1519-1539 ◽  
Author(s):  
Bruce A. Wielicki ◽  
D. F. Young ◽  
M. G. Mlynczak ◽  
K. J. Thome ◽  
S. Leroy ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Climate Variables ◽  
Absolute Accuracy ◽  
Calibration Laboratory ◽  
Statistical Confidence ◽  
Uncertainty Sources ◽  
The Earth ◽  
Wide Range ◽  
Decadal Climate

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission will provide a calibration laboratory in orbit for the purpose of accurately measuring and attributing climate change. CLARREO measurements establish new climate change benchmarks with high absolute radiometric accuracy and high statistical confidence across a wide range of essential climate variables. CLARREO's inherently high absolute accuracy will be verified and traceable on orbit to Système Internationale (SI) units. The benchmarks established by CLARREO will be critical for assessing changes in the Earth system and climate model predictive capabilities for decades into the future as society works to meet the challenge of optimizing strategies for mitigating and adapting to climate change. The CLARREO benchmarks are derived from measurements of the Earth's thermal infrared spectrum (5–50 μm), the spectrum of solar radiation reflected by the Earth and its atmosphere (320–2300 nm), and radio occultation refractivity from which accurate temperature profiles are derived. The mission has the ability to provide new spectral fingerprints of climate change, as well as to provide the first orbiting radiometer with accuracy sufficient to serve as the reference transfer standard for other space sensors, in essence serving as a “NIST [National Institute of Standards and Technology] in orbit.” CLARREO will greatly improve the accuracy and relevance of a wide range of space-borne instruments for decadal climate change. Finally, CLARREO has developed new metrics and methods for determining the accuracy requirements of climate observations for a wide range of climate variables and uncertainty sources. These methods should be useful for improving our understanding of observing requirements for most climate change observations.

scholarly journals Climate Change Hotspots Identification in China through the CMIP5 Global Climate Model Ensemble

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Huanghe Gu ◽  
Zhongbo Yu ◽  
Jigan Wang ◽  
Qin Ju ◽  
Chuanguo Yang ◽  
...  
Keyword(s):  
Climate Change ◽  
21St Century ◽  
Climate Model ◽  
Greenhouse Gas Emission ◽  
Global Climate Model ◽  
Regional Climate ◽  
Regional Climate Change ◽  
Seasonal Temperature ◽  
Climate Variables ◽  
Model Ensemble

China is one of the countries vulnerable to adverse climate changes. The potential climate change hotspots in China throughout the 21st century are identified in this study by using a multimodel, multiscenario climate model ensemble that includes Phase Five of the Coupled Model Intercomparison Project (CMIP5) atmosphere-ocean general circulation models. Both high (RCP8.5) and low (RCP4.5) greenhouse gas emission trajectories are tested, and both the mean and extreme seasonal temperature and precipitation are considered in identifying regional climate change hotspots. Tarim basin and Tibetan Plateau in West China are identified as persistent regional climate change hotspots in both the RCP4.5 and RCP8.5 scenarios. The aggregate impacts of climate change increase throughout the 21st century and are more significant in RCP8.5 than in RCP4.5. Extreme hot event and mean temperature are two climate variables that greatly contribute to the hotspots calculation in all regions. The contribution of other climate variables exhibits a notable subregional variability. South China is identified as another hotspot based on the change of extreme dry event, especially in SON and DJF, which indicates that such event will frequently occur in the future. Our results can contribute to the designing of national and cross-national adaptation and mitigation policies.

scholarly journals Detection of Atmospheric Changes in Spatially and Temporally Averaged Infrared Spectra Observed from Space

2011 ◽  
Vol 24 (24) ◽  
pp. 6392-6407 ◽  
Author(s):  
Seiji Kato ◽  
Bruce A. Wielicki ◽  
Fred G. Rose ◽  
Xu Liu ◽  
Patrick C. Taylor ◽  
...  
Keyword(s):  
Linear Regression ◽  
Climate Model ◽  
Spectral Radiance ◽  
Percentage Error ◽  
Small Scale ◽  
Atmospheric Variability ◽  
Cloud Property ◽  
Cloud Properties ◽  
Property Changes ◽  
Atmospheric Changes

Abstract Variability present at a satellite instrument sampling scale (small-scale variability) has been neglected in earlier simulations of atmospheric and cloud property change retrievals using spatially and temporally averaged spectral radiances. The effects of small-scale variability in the atmospheric change detection process are evaluated in this study. To simulate realistic atmospheric variability, top-of-the-atmosphere nadir-view longwave spectral radiances are computed at a high temporal (instantaneous) resolution with a 20-km field-of-view using cloud properties retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) measurements, along with temperature humidity profiles obtained from reanalysis. Specifically, the effects of the variability on the necessary conditions for retrieving atmospheric changes by a linear regression are tested. The percentage error in the annual 10° zonal mean spectral radiance difference obtained by assuming linear combinations of individual perturbations expressed as a root-mean-square (RMS) difference computed over wavenumbers between 200 and 2000 cm−1 is 10%–15% for most of the 10° zones. However, if cloud fraction perturbation is excluded, the RMS difference decreases to less than 2%. Monthly and annual 10° zonal mean spectral radiances change linearly with atmospheric property perturbations, which occur when atmospheric properties are perturbed by an amount approximately equal to the variability of the10° zonal monthly deseasonalized anomalies or by a climate-model-predicted decadal change. Nonlinear changes in the spectral radiances of magnitudes similar to those obtained through linear estimation can arise when cloud heights and droplet radii in water cloud change. The spectral shapes computed by perturbing different atmospheric and cloud properties are different so that linear regression can separate individual spectral radiance changes from the sum of the spectral radiance change. When the effects of small-scale variability are treated as noise, however, the error in retrieved cloud properties is large. The results suggest the importance of considering small-scale variability in inferring atmospheric and cloud property changes from the satellite-observed zonally and annually averaged spectral radiance difference.

Distributed climate change impacts and uncertainty throughout mountainous catchments

2020 ◽  
Author(s):  
Jorge Sebastián Moraga ◽  
Nadav Peleg ◽  
Simone Fatichi ◽  
Peter Molnar ◽  
Paolo Burlando
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Climate Change Signal ◽  
Similar Amount ◽  
Climate Variables ◽  
Distributed Hydrological Model ◽  
Space And Time ◽  
Mountainous Catchments ◽  
Impacts Of Climate Change

<p>A combination of high-resolution models in space and time was used to evaluate the impacts of climate change on streamflow statistics and their uncertainties throughout three mountainous catchments in Switzerland (Thur, K. Emme and Maggia). The two-dimensional AWE-GEN-2d model was used to simulate ensembles of gridded climate variables at an hourly and 2-km resolution based on ground and remote-sensing observations. The model was re-parametrized using the “factors of change” approach, calculated from regional climate models, and it was used to simulate ensembles of climate data until the end of the 21st century. These ensembles were subsequently used as inputs into the fully distributed hydrological model Topkapi-ETH, which is suitable for simulating streamflow over complex terrain, and considers all the relevant hydrological processes. Based on large ensembles of simulated hydrological variables, the changes of the hydrological components in space and time were evaluated along with their uncertainty due to the internal variability of the climate and the climate model selection. Results indicate a rather uniform increase in temperature for all catchments, characterized by high uncertainty toward the end of the century (with strongest increases of over 5°C). On the other hand, the magnitude and spatial patterns (namely, mountain vs valley) of change in precipitation differ between catchments, and the uncertainty of changes in extreme events is of larger magnitude than the climate change signal. The changes in climate are foreseen to affect the hydrological components in the catchments: evapotranspiration is projected to increase, while snowmelt contribution to the streamflow is expected to decrease by 50% at the end of the century. Model results indicate a decrease in streamflow at the outlet during the summer months and an increase in winter as early as the 2020-2049 period. Conversely, changes in extreme discharge show an uncertainty greater than the change signal for most climate models. Spatially heterogeneous changes in temperature and precipitation lead to elevation-dependent hydrological responses: e.g., streamflow annual means would decrease 20% in the upper reaches of the Thur catchment, while decreasing a similar amount in the downstream reaches. Correspondingly, hourly extremes are expected to decrease 20% in the upper reaches and increase up to 50% in the lowest part of the catchment. However, the signals of the change for extreme streamflow, compared to their uncertainty, are stronger for the upper parts of the river network. These results illustrate the benefit of using stochastic downscaling of climate variables to capture climate variability and assess uncertainty, and emphasize the importance of investigating the distributed impacts of climate change in mountainous areas, which may differ between high and low elevation reaches. </p>

scholarly journals Charting pathways to climate change mitigation in a coupled socio-climate model

2019 ◽  
Vol 15 (6) ◽  
pp. e1007000 ◽  
Author(s):  
Thomas M. Bury ◽  
Chris T. Bauch ◽  
Madhur Anand
Keyword(s):  
Climate Change ◽  
Climate Change Mitigation ◽  
Climate Model ◽  
Change Mitigation

scholarly journals Incorrect Asian aerosols affecting the attribution and projection of regional climate change in CMIP6 models

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Zhili Wang ◽  
Lei Lin ◽  
Yangyang Xu ◽  
Huizheng Che ◽  
Xiaoye Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
Radiative Forcing ◽  
Impact Analysis ◽  
Climate Model ◽  
Regional Climate ◽  
Eastern China ◽  
Coupled Model ◽  
Regional Climate Change ◽  
Anthropogenic Aerosol ◽  
Wide Range

AbstractAnthropogenic aerosol (AA) forcing has been shown as a critical driver of climate change over Asia since the mid-20th century. Here we show that almost all Coupled Model Intercomparison Project Phase 6 (CMIP6) models fail to capture the observed dipole pattern of aerosol optical depth (AOD) trends over Asia during 2006–2014, last decade of CMIP6 historical simulation, due to an opposite trend over eastern China compared with observations. The incorrect AOD trend over China is attributed to problematic AA emissions adopted by CMIP6. There are obvious differences in simulated regional aerosol radiative forcing and temperature responses over Asia when using two different emissions inventories (one adopted by CMIP6; the other from Peking university, a more trustworthy inventory) to driving a global aerosol-climate model separately. We further show that some widely adopted CMIP6 pathways (after 2015) also significantly underestimate the more recent decline in AA emissions over China. These flaws may bring about errors to the CMIP6-based regional climate attribution over Asia for the last two decades and projection for the next few decades, previously anticipated to inform a wide range of impact analysis.

scholarly journals The impact of climate change in wheat and barley yields in the Iberian Peninsula

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Virgílio A. Bento ◽  
Andreia F. S. Ribeiro ◽  
Ana Russo ◽  
Célia M. Gouveia ◽  
Rita M. Cardoso ◽  
...  
Keyword(s):  
Climate Change ◽  
Iberian Peninsula ◽  
Climate Model ◽  
Global Climate Models ◽  
Maximum Temperature ◽  
Historical Period ◽  
Early Winter ◽  
Impact Of Climate Change ◽  
The Impact ◽  
Spring Maximum

AbstractThe impact of climate change on wheat and barley yields in two regions of the Iberian Peninsula is here examined. Regression models are developed by using EURO-CORDEX regional climate model (RCM) simulations, forced by ERA-Interim, with monthly maximum and minimum air temperatures and monthly accumulated precipitation as predictors. Additionally, RCM simulations forced by different global climate models for the historical period (1972–2000) and mid-of-century (2042–2070; under the two emission scenarios RCP4.5 and RCP8.5) are analysed. Results point to different regional responses of wheat and barley. In the southernmost regions, results indicate that the main yield driver is spring maximum temperature, while further north a larger dependence on spring precipitation and early winter maximum temperature is observed. Climate change seems to induce severe yield losses in the southern region, mainly due to an increase in spring maximum temperature. On the contrary, a yield increase is projected in the northern regions, with the main driver being early winter warming that stimulates earlier growth. These results warn on the need to implement sustainable agriculture policies, and on the necessity of regional adaptation strategies.

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