scholarly journals Climate Forcings and Climate Sensitivities Diagnosed from Coupled Climate Model Integrations

2006 ◽  
Vol 19 (23) ◽  
pp. 6181-6194 ◽  
Author(s):  
Piers Mde F. Forster ◽  
Karl E. Taylor
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Surface Temperature ◽  
Radiative Forcing ◽  
Climate Forcing ◽  
Lapse Rate ◽  
Climate Feedback ◽  
Short Time Scale ◽  
Climate Change Scenarios ◽  
Climate Forcings

Abstract A simple technique is proposed for calculating global mean climate forcing from transient integrations of coupled atmosphere–ocean general circulation models (AOGCMs). This “climate forcing” differs from the conventionally defined radiative forcing as it includes semidirect effects that account for certain short time scale responses in the troposphere. First, a climate feedback term is calculated from reported values of 2 × CO2 radiative forcing and surface temperature time series from 70-yr simulations by 20 AOGCMs. In these simulations carbon dioxide is increased by 1% yr−1. The derived climate feedback agrees well with values that are diagnosed from equilibrium climate change experiments of slab-ocean versions of the same models. These climate feedback terms are associated with the fast, quasi-linear response of lapse rate, clouds, water vapor, and albedo to global surface temperature changes. The importance of the feedbacks is gauged by their impact on the radiative fluxes at the top of the atmosphere. Partial compensation is found between longwave and shortwave feedback terms that lessens the intermodel differences in the equilibrium climate sensitivity. There is also some indication that the AOGCMs overestimate the strength of the positive longwave feedback. These feedback terms are then used to infer the shortwave and longwave time series of climate forcing in twentieth- and twenty-first-century simulations in the AOGCMs. The technique is validated using conventionally calculated forcing time series from four AOGCMs. In these AOGCMs the shortwave and longwave climate forcings that are diagnosed agree with the conventional forcing time series within ∼10%. The shortwave forcing time series exhibit order of magnitude variations between the AOGCMs, differences likely related to how both natural forcings and/or anthropogenic aerosol effects are included. There are also factor of 2 differences in the longwave climate forcing time series, which may indicate problems with the modeling of well-mixed greenhouse gas changes. The simple diagnoses presented provides an important and useful first step for understanding differences in AOGCM integrations, indicating that some of the differences in model projections can be attributed to different prescribed climate forcing, even for so-called standard climate change scenarios.

scholarly journals The Climate Sensitivity and Its Components Diagnosed from Earth Radiation Budget Data

2006 ◽  
Vol 19 (1) ◽  
pp. 39-52 ◽  
Author(s):  
Piers Mde F. Forster ◽  
Jonathan M. Gregory
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Surface Temperature ◽  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Radiation Budget ◽  
Climate Feedback ◽  
Feedback Parameter ◽  
Earth Radiation Budget ◽  
Earth Radiation

Abstract One of the major uncertainties in the ability to predict future climate change, and hence its impacts, is the lack of knowledge of the earth’s climate sensitivity. Here, data are combined from the 1985–96 Earth Radiation Budget Experiment (ERBE) with surface temperature change information and estimates of radiative forcing to diagnose the climate sensitivity. Importantly, the estimate is completely independent of climate model results. A climate feedback parameter of 2.3 ± 1.4 W m−2 K−1 is found. This corresponds to a 1.0–4.1-K range for the equilibrium warming due to a doubling of carbon dioxide (assuming Gaussian errors in observable parameters, which is approximately equivalent to a uniform “prior” in feedback parameter). The uncertainty range is due to a combination of the short time period for the analysis as well as uncertainties in the surface temperature time series and radiative forcing time series, mostly the former. Radiative forcings may not all be fully accounted for; however, an argument is presented that the estimate of climate sensitivity is still likely to be representative of longer-term climate change. The methodology can be used to 1) retrieve shortwave and longwave components of climate feedback and 2) suggest clear-sky and cloud feedback terms. There is preliminary evidence of a neutral or even negative longwave feedback in the observations, suggesting that current climate models may not be representing some processes correctly if they give a net positive longwave feedback.

scholarly journals The potential habitat of desert locusts is contracting: predictions under climate change scenarios

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12311
Author(s):  
Jingyun Guan ◽  
Moyan Li ◽  
Xifeng Ju ◽  
Jun Lin ◽  
Jianguo Wu ◽  
...  
Keyword(s):  
Climate Change ◽  
Food Security ◽  
Radiative Forcing ◽  
Social Impact ◽  
Regional Cooperation ◽  
Extreme Weather Events ◽  
Climate Change Scenarios ◽  
Desert Locust ◽  
Maxent Model ◽  
Potential Habitat

Desert locusts are notorious for their widespread distribution and strong destructive power. Their influence extends from the vast arid and semiarid regions of western Africa to northwestern India. Large-scale locust outbreaks can have devastating consequences for food security, and their social impact may be long-lasting. Climate change has increased the uncertainty of desert locust outbreaks, and predicting suitable habitats for this species under climate change scenarios will help humans deal with the potential threat of locust outbreaks. By comprehensively considering climate, soil, and terrain variables, the maximum entropy (MaxEnt) model was used to predict the potential habitats of solitary desert locusts in the 2050s and 2070s under the four shared socioeconomic pathways (SSP126, SSP245, SSP370, and SSP585) in the CMIP6 model. The modeling results show that the average area under the curve (AUC) and true skill statistic (TSS) reached 0.908 ± 0.002 and 0.701, respectively, indicating that the MaxEnt model performed extremely well and provided outstanding prediction results. The prediction results indicate that climate change will have an impact on the distribution of the potential habitat of solitary desert locusts. With the increase in radiative forcing overtime, the suitable areas for desert locusts will continue to contract, especially in the 2070s under the SSP585 scenario, and the moderately and highly suitable areas will decrease by 0.88 × 106 km2 and 1.55 × 106 km2, respectively. Although the potentially suitable area for desert locusts is contracting, the future threat posed by the desert locust to agricultural production and food security cannot be underestimated, given the combination of maintained breeding areas, frequent extreme weather events, pressure from population growth, and volatile sociopolitical environments. In conclusion, methods such as monitoring and early warning, financial support, regional cooperation, and scientific prevention and control of desert locust plagues should be further implemented.

scholarly journals Future Runoff Analysis in the Mekong River Basin under a Climate Change Scenario Using Deep Learning

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1556 ◽  
Author(s):  
Daeeop Lee ◽  
Giha Lee ◽  
Seongwon Kim ◽  
Sungho Jung
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Climate Change Scenario ◽  
Swat Model ◽  
Mekong River ◽  
Change Scenario ◽  
Climate Change Scenarios ◽  
Rainfall Runoff ◽  
Runoff Prediction ◽  
Runoff Analysis

In establishing adequate climate change policies regarding water resource development and management, the most essential step is performing a rainfall-runoff analysis. To this end, although several physical models have been developed and tested in many studies, they require a complex grid-based parameterization that uses climate, topography, land-use, and geology data to simulate spatiotemporal runoff. Furthermore, physical rainfall-runoff models also suffer from uncertainty originating from insufficient data quality and quantity, unreliable parameters, and imperfect model structures. As an alternative, this study proposes a rainfall-runoff analysis system for the Kratie station on the Mekong River mainstream using the long short-term memory (LSTM) model, a data-based black-box method. Future runoff variations were simulated by applying a climate change scenario. To assess the applicability of the LSTM model, its result was compared with a runoff analysis using the Soil and Water Assessment Tool (SWAT) model. The following steps (dataset periods in parentheses) were carried out within the SWAT approach: parameter correction (2000–2005), verification (2006–2007), and prediction (2008–2100), while the LSTM model went through the process of training (1980–2005), verification (2006–2007), and prediction (2008–2100). Globally available data were fed into the algorithms, with the exception of the observed discharge and temperature data, which could not be acquired. The bias-corrected Representative Concentration Pathways (RCPs) 4.5 and 8.5 climate change scenarios were used to predict future runoff. When the reproducibility at the Kratie station for the verification period of the two models (2006–2007) was evaluated, the SWAT model showed a Nash–Sutcliffe efficiency (NSE) value of 0.84, while the LSTM model showed a higher accuracy, NSE = 0.99. The trend analysis result of the runoff prediction for the Kratie station over the 2008–2100 period did not show a statistically significant trend for neither scenario nor model. However, both models found that the annual mean flow rate in the RCP 8.5 scenario showed greater variability than in the RCP 4.5 scenario. These findings confirm that the LSTM runoff prediction presents a higher reproducibility than that of the SWAT model in simulating runoff variation according to time-series changes. Therefore, the LSTM model, which derives relatively accurate results with a small amount of data, is an effective approach to large-scale hydrologic modeling when only runoff time-series are available.

scholarly journals Anthropogenic and Natural Radiative Forcing: Positive Feedbacks

2018 ◽  
Vol 6 (4) ◽  
pp. 146 ◽  
Author(s):  
Jean-Louis Pinault
Keyword(s):  
Surface Temperature ◽  
Radiative Forcing ◽  
Rossby Waves ◽  
Temperature Response ◽  
Heat Fluxes ◽  
Lapse Rate ◽  
Positive Feedbacks ◽  
Long Period ◽  
Sea Surface Temperature Anomalies ◽  
The Impact

This article is based on recent work intended to estimate the impact of solar forcing mediated by long-period ocean Rossby waves that are resonantly forced—the ‘Gyral Rossby Waves’ (GRWs). Here, we deduce both the part of the anthropogenic and climate components within the instrumental surface temperature spatial patterns. The natural variations in temperature are estimated from a weighted sum of sea surface temperature anomalies in preselected areas of subtropical gyres representative of long-period GRWs. The temperature response to anthropogenic forcing is deduced by subtracting the climate component from the instrumental temperature. Depending on whether the inland regions are primarily impacted by latent or sensible heat fluxes from the oceans, positive feedbacks occur. This suggests that the lapse rate and the high troposphere cloud cover have a driving role in the amplification effect of anthropogenic climate forcing, while specifying the involved mechanisms.

scholarly journals Spatiotemporal Variability of Land Surface Albedo over the Tibet Plateau from 2001 to 2019

2020 ◽  
Vol 12 (7) ◽  
pp. 1188
Author(s):  
Xingwen Lin ◽  
Jianguang Wen ◽  
Qinhuo Liu ◽  
Dongqin You ◽  
Shengbiao Wu ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Land Cover ◽  
Snow Cover ◽  
Land Surface ◽  
Radiative Forcing ◽  
Surface Albedo ◽  
Tibet Plateau ◽  
Land Cover Types ◽  
Yearly Average

As an essential climate variable (ECV), land surface albedo plays an important role in the Earth surface radiation budget and regional or global climate change. The Tibetan Plateau (TP) is a sensitive environment to climate change, and understanding its albedo seasonal and inter-annual variations is thus important to help capture the climate change rules. In this paper, we analyzed the large-scale spatial patterns, temporal trends, and seasonal variability of land surface albedo overall the TP, based on the moderate resolution imaging spectroradiometer (MODIS) MCD43 albedo products from 2001 to 2019. Specifically, we assessed the correlations between the albedo anomaly and the anomalies of normalized difference vegetation index (NDVI), the fraction of snow cover (snow cover), and land surface temperature (LST). The results show that there are larger albedo variations distributed in the mountainous terrain of the TP. Approximately 10.06% of the land surface is identified to have been influenced by the significant albedo variation from the year 2001 to 2019. The yearly averaged albedo was decreased significantly at a rate of 0.0007 (Sen’s slope) over the TP. Additionally, the yearly average snow cover was decreased at a rate of 0.0756. However, the yearly average NDVI and LST were increased with slopes of 0.0004 and 0.0253 over the TP, respectively. The relative radiative forcing (RRF) caused by the land cover change (LCC) is larger than that caused by gradual albedo variation in steady land cover types. Overall, the RRF due to gradual albedo variation varied from 0.0005 to 0.0170 W/m2, and the RRF due to LCC variation varied from 0.0037 to 0.0243 W/m2 during the years 2001 to 2019. The positive RRF caused by gradual albedo variation or the LCC can strengthen the warming effects in the TP. The impact of the gradual albedo variations occurring in the steady land cover types was very low between 2001 and 2019 because the time series was short, and it therefore cannot be neglected when examining radiative forcing for a long time series regarding climate change.

scholarly journals Time-Varying Climate Sensitivity from Regional Feedbacks

2013 ◽  
Vol 26 (13) ◽  
pp. 4518-4534 ◽  
Author(s):  
Kyle C. Armour ◽  
Cecilia M. Bitz ◽  
Gerard H. Roe
Keyword(s):  
Surface Temperature ◽  
Climate Sensitivity ◽  
Time Variation ◽  
Radiative Forcing ◽  
Global Climate ◽  
Regional Climate ◽  
Climate Feedback ◽  
Climate Feedbacks ◽  
Geographic Pattern ◽  
Surface Warming

Abstract The sensitivity of global climate with respect to forcing is generally described in terms of the global climate feedback—the global radiative response per degree of global annual mean surface temperature change. While the global climate feedback is often assumed to be constant, its value—diagnosed from global climate models—shows substantial time variation under transient warming. Here a reformulation of the global climate feedback in terms of its contributions from regional climate feedbacks is proposed, providing a clear physical insight into this behavior. Using (i) a state-of-the-art global climate model and (ii) a low-order energy balance model, it is shown that the global climate feedback is fundamentally linked to the geographic pattern of regional climate feedbacks and the geographic pattern of surface warming at any given time. Time variation of the global climate feedback arises naturally when the pattern of surface warming evolves, actuating feedbacks of different strengths in different regions. This result has substantial implications for the ability to constrain future climate changes from observations of past and present climate states. The regional climate feedbacks formulation also reveals fundamental biases in a widely used method for diagnosing climate sensitivity, feedbacks, and radiative forcing—the regression of the global top-of-atmosphere radiation flux on global surface temperature. Further, it suggests a clear mechanism for the “efficacies” of both ocean heat uptake and radiative forcing.

scholarly journals The Ocean’s Role in Continental Climate Variability and Change

2009 ◽  
Vol 22 (18) ◽  
pp. 4939-4952 ◽  
Author(s):  
Dietmar Dommenget
Keyword(s):  
Global Warming ◽  
Climate Variability ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Radiative Forcing ◽  
Global Scale ◽  
Future Climate Change ◽  
Climate Change Scenarios ◽  
Continental Climate

Abstract A characteristic feature of global warming is the land–sea contrast, with stronger warming over land than over oceans. Recent studies find that this land–sea contrast also exists in equilibrium global change scenarios, and it is caused by differences in the availability of surface moisture over land and oceans. In this study it is illustrated that this land–sea contrast exists also on interannual time scales and that the ocean–land interaction is strongly asymmetric. The land surface temperature is more sensitive to the oceans than the oceans are to the land surface temperature, which is related to the processes causing the land–sea contrast in global warming scenarios. It suggests that the ocean’s natural variability and change is leading to variability and change with enhanced magnitudes over the continents, causing much of the longer-time-scale (decadal) global-scale continental climate variability. Model simulations illustrate that continental warming due to anthropogenic forcing (e.g., the warming at the end of the last century or future climate change scenarios) is mostly (80%–90%) indirectly forced by the contemporaneous ocean warming, not directly by local radiative forcing.

scholarly journals How Well Do We Understand and Evaluate Climate Change Feedback Processes?

2006 ◽  
Vol 19 (15) ◽  
pp. 3445-3482 ◽  
Author(s):  
Sandrine Bony ◽  
Robert Colman ◽  
Vladimir M. Kattsov ◽  
Richard P. Allan ◽  
Christopher S. Bretherton ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Sensitivity ◽  
General Circulation ◽  
External Perturbation ◽  
General Circulation Models ◽  
Lapse Rate ◽  
Climate Feedback ◽  
Climate Feedbacks ◽  
Intergovernmental Panel ◽  
Global Estimates

Abstract Processes in the climate system that can either amplify or dampen the climate response to an external perturbation are referred to as climate feedbacks. Climate sensitivity estimates depend critically on radiative feedbacks associated with water vapor, lapse rate, clouds, snow, and sea ice, and global estimates of these feedbacks differ among general circulation models. By reviewing recent observational, numerical, and theoretical studies, this paper shows that there has been progress since the Third Assessment Report of the Intergovernmental Panel on Climate Change in (i) the understanding of the physical mechanisms involved in these feedbacks, (ii) the interpretation of intermodel differences in global estimates of these feedbacks, and (iii) the development of methodologies of evaluation of these feedbacks (or of some components) using observations. This suggests that continuing developments in climate feedback research will progressively help make it possible to constrain the GCMs’ range of climate feedbacks and climate sensitivity through an ensemble of diagnostics based on physical understanding and observations.

scholarly journals A new energy-balance approach to linear filtering for estimating effective radiative forcing from temperature time series

2020 ◽  
Vol 6 (2) ◽  
pp. 91-102
Author(s):  
Donald P. Cummins ◽  
David B. Stephenson ◽  
Peter A. Stott
Keyword(s):  
Time Series ◽  
Energy Balance ◽  
Surface Temperature ◽  
Radiative Forcing ◽  
Earth System ◽  
Linear Filtering ◽  
Past Climate ◽  
System Models ◽  
Effective Radiative Forcing ◽  
Earth System Models

Abstract. Reliable estimates of historical effective radiative forcing (ERF) are important for understanding the causes of past climate change and for constraining predictions of future warming. This study proposes a new linear-filtering method for estimating historical radiative forcing from time series of global mean surface temperature (GMST), using energy-balance models (EBMs) fitted to GMST from CO2-quadrupling general circulation model (GCM) experiments. We show that the response of any k-box EBM can be represented as an ARMA(k, k−1) (autoregressive moving-average) filter. We show how, by inverting an EBM's ARMA filter representation, time series of surface temperature may be converted into radiative forcing. The method is illustrated using three-box EBM fits to two recent Earth system models from CMIP5 and CMIP6 (Coupled Model Intercomparison Project). A comparison with published results obtained using the established ERF_trans method, a purely GCM-based approach, shows that our new method gives an ERF time series that closely matches the GCM-based series (correlation of 0.83). Time series of estimated historical ERF are obtained by applying the method to a dataset of historical temperature observations. The results show that there is clear evidence of a significant increase over the historical period with an estimated forcing in 2018 of 1.45±0.504 W m−2 when derived using the two Earth system models. This method could be used in the future to attribute past climate changes to anthropogenic and natural factors and to help constrain estimates of climate sensitivity.

scholarly journals Analysis of Climate Change Effects on Surface Temperature in Central-Italy Lakes Using Satellite Data Time-Series

2021 ◽  
Vol 14 (1) ◽  
pp. 117
Author(s):  
Davide De Santis ◽  
Fabio Del Frate ◽  
Giovanni Schiavon
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Surface Temperature ◽  
Satellite Data ◽  
Water Surface ◽  
Central Italy ◽  
Large Lakes ◽  
Climate Change Effects ◽  
Surface Temperatures ◽  
The Impact

Evaluation of the impact of climate change on water bodies has been one of the most discussed open issues of recent years. The exploitation of satellite data for the monitoring of water surface temperatures, combined with ground measurements where available, has already been shown in several previous studies, but these studies mainly focused on large lakes around the world. In this work the water surface temperature characterization during the last few decades of two small–medium Italian lakes, Lake Bracciano and Lake Martignano, using satellite data is addressed. The study also takes advantage of the last space-borne platforms, such as Sentinel-3. Long time series of clear sky conditions and atmospherically calibrated (using a simplified Planck’s Law-based algorithm) images were processed in order to derive the lakes surface temperature trends from 1984 to 2019. The results show an overall increase in water surface temperatures which is more evident on the smallest and shallowest of the two test sites. In particular, it was observed that, since the year 2000, the surface temperature of both lakes has risen by about 0.106 °C/year on average, which doubles the rate that can be retrieved by considering the whole period 1984–2019 (0.053 °C/year on average).

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