scholarly journals Climate Model Dependence and the Ensemble Dependence Transformation of CMIP Projections

2015 ◽  
Vol 28 (6) ◽  
pp. 2332-2348 ◽  
Author(s):  
G. Abramowitz ◽  
C. H. Bishop
Keyword(s):  
Climate Model ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
First Century ◽  
Annual Rainfall ◽  
Model Code ◽  
Temperature And Precipitation ◽  
Ensemble Mean ◽  
Out Of Sample ◽  
Twenty First Century

Abstract Obtaining multiple estimates of future climate for a given emissions scenario is key to understanding the likelihood and uncertainty associated with climate-related impacts. This is typically done by collating model estimates from different research institutions internationally with the assumption that they constitute independent samples. Heuristically, however, several factors undermine this assumption: shared treatment of processes between models, shared observed data for evaluation, and even shared model code. Here, a “perfect model” approach is used to test whether a previously proposed ensemble dependence transformation (EDT) can improve twenty-first-century Coupled Model Intercomparison Project (CMIP) projections. In these tests, where twenty-first-century model simulations are used as out-of-sample “observations,” the mean-square difference between the transformed ensemble mean and “observations” is on average 30% less than for the untransformed ensemble mean. In addition, the variance of the transformed ensemble matches the variance of the ensemble mean about the “observations” much better than in the untransformed ensemble. Results show that the EDT has a significant effect on twenty-first-century projections of both surface air temperature and precipitation. It changes projected global average temperature increases by as much as 16% (0.2°C for B1 scenario), regional average temperatures by as much as 2.6°C (RCP8.5 scenario), and regional average annual rainfall by as much as 410 mm (RCP6.0 scenario). In some regions, however, the effect is minimal. It is also found that the EDT causes changes to temperature projections that differ in sign for different emissions scenarios. This may be as much a function of the makeup of the ensembles as the nature of the forcing conditions.

scholarly journals A Simple, Coherent Framework for Partitioning Uncertainty in Climate Predictions

2011 ◽  
Vol 24 (17) ◽  
pp. 4634-4643 ◽  
Author(s):  
Stan Yip ◽  
Christopher A. T. Ferro ◽  
David B. Stephenson ◽  
Ed Hawkins
Keyword(s):  
Model Uncertainty ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
Internal Variability ◽  
First Century ◽  
Uncertainty Model ◽  
Ensemble Mean ◽  
Scenario Uncertainty ◽  
Twenty First Century ◽  
Multimodel Ensembles

A simple and coherent framework for partitioning uncertainty in multimodel climate ensembles is presented. The analysis of variance (ANOVA) is used to decompose a measure of total variation additively into scenario uncertainty, model uncertainty, and internal variability. This approach requires fewer assumptions than existing methods and can be easily used to quantify uncertainty related to model–scenario interaction—the contribution to model uncertainty arising from the variation across scenarios of model deviations from the ensemble mean. Uncertainty in global mean surface air temperature is quantified as a function of lead time for a subset of the Coupled Model Intercomparison Project phase 3 ensemble and results largely agree with those published by other authors: scenario uncertainty dominates beyond 2050 and internal variability remains approximately constant over the twenty-first century. Both elements of model uncertainty, due to scenario-independent and scenario-dependent deviations from the ensemble mean, are found to increase with time. Estimates of model deviations that arise as by-products of the framework reveal significant differences between models that could lead to a deeper understanding of the sources of uncertainty in multimodel ensembles. For example, three models show a diverging pattern over the twenty-first century, while another model exhibits an unusually large variation among its scenario-dependent deviations.

scholarly journals Seasonal Responses of Terrestrial Carbon Cycle to Climate Variations in CMIP5 Models: Evaluation and Projection

2017 ◽  
Vol 30 (16) ◽  
pp. 6481-6503 ◽  
Author(s):  
Yongwen Liu ◽  
Shilong Piao ◽  
Xu Lian ◽  
Philippe Ciais ◽  
W. Kolby Smith
Keyword(s):  
Temperature Sensitivity ◽  
Boreal Forests ◽  
Net Primary Production ◽  
Coupled Model ◽  
First Century ◽  
Cmip5 Models ◽  
Temperature And Precipitation ◽  
Northern High Latitude ◽  
Twenty First Century ◽  
Subarctic Ecosystems

Seventeen Earth system models (ESMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were evaluated, focusing on the seasonal sensitivities of net biome production (NBP), net primary production (NPP), and heterotrophic respiration (Rh) to interannual variations in temperature and precipitation during 1982–2005 and their changes over the twenty-first century. Temperature sensitivity of NPP in ESMs was generally consistent across northern high-latitude biomes but significantly more negative for tropical and subtropical biomes relative to satellite-derived estimates. The temperature sensitivity of NBP in both inversion-based and ESM estimates was generally consistent in March–May (MAM) and September–November (SON) for tropical forests, semiarid ecosystems, and boreal forests. By contrast, for inversion-based NBP estimates, temperature sensitivity of NBP was nonsignificant for June–August (JJA) for all biomes except boreal forest; whereas, for ESM NBP estimates, the temperature sensitivity for JJA was significantly negative for all biomes except shrublands and subarctic ecosystems. Both satellite-derived NPP and inversion-based NBP are often decoupled from precipitation, whereas ESM NPP and NBP estimates are generally positively correlated with precipitation, suggesting that ESMs are oversensitive to precipitation. Over the twenty-first century, changes in temperature sensitivities of NPP, Rh, and NBP are consistent across all RCPs but stronger under more intensive scenarios. The temperature sensitivity of NBP was found to decrease in tropics and subtropics and increase in northern high latitudes in MAM due to an increased temperature sensitivity of NPP. Across all biomes, projected temperature sensitivity of NPP decreased in JJA and SON. Projected precipitation sensitivity of NBP did not change across biomes, except over grasslands in MAM.

scholarly journals Future Changes in Climate over the Arabian Peninsula based on CMIP6 Multimodel Simulations

2020 ◽  
Vol 4 (4) ◽  
pp. 611-630
Author(s):  
Mansour Almazroui ◽  
M. Nazrul Islam ◽  
Sajjad Saeed ◽  
Fahad Saeed ◽  
Muhammad Ismail
Keyword(s):  
Confidence Level ◽  
Climate Models ◽  
Arabian Peninsula ◽  
Ghg Emissions ◽  
Coupled Model ◽  
First Century ◽  
Cmip5 Models ◽  
Temperature And Precipitation ◽  
Twenty First Century ◽  
Far Future

AbstractThis paper presents the changes in projected temperature and precipitation over the Arabian Peninsula for the twenty-first century using the Coupled Model Intercomparison Project phase 6 (CMIP6) dataset. The changes are obtained by analyzing the multimodel ensemble from 31 CMIP6 models for the near (2030–2059) and far (2070–2099) future periods, with reference to the base period 1981–2010, under three future Shared Socioeconomic Pathways (SSPs). Observations show that the annual temperature is rising at the rate of 0.63 ˚C decade–1 (significant at the 99% confidence level), while annual precipitation is decreasing at the rate of 6.3 mm decade–1 (significant at the 90% confidence level), averaged over Saudi Arabia. For the near (far) future period, the 66% likely ranges of annual-averaged temperature is projected to increase by 1.2–1.9 (1.2–2.1) ˚C, 1.4–2.1 (2.3–3.4) ˚C, and 1.8–2.7 (4.1–5.8) ˚C under SSP1–2.6, SSP2–4.5, and SSP5–8.5, respectively. Higher warming is projected in the summer than in the winter, while the Northern Arabian Peninsula (NAP) is projected to warm more than Southern Arabian Peninsula (SAP), by the end of the twenty-first century. For precipitation, a dipole-like pattern is found, with a robust increase in annual mean precipitation over the SAP, and a decrease over the NAP. The 66% likely ranges of annual-averaged precipitation over the whole Arabian Peninsula is projected to change by 5 to 28 (–3 to 29) %, 5 to 31 (4 to 49) %, and 1 to 38 (12 to 107) % under SSP1–2.6, SSP2–4.5, and SSP5–8.5, respectively, in the near (far) future. Overall, the full ranges in CMIP6 remain higher than the CMIP5 models, which points towards a higher climate sensitivity of some of the CMIP6 climate models to greenhouse gas (GHG) emissions as compared to the CMIP5. The CMIP6 dataset confirmed previous findings of changes in future climate over the Arabian Peninsula based on CMIP3 and CMIP5 datasets. The results presented in this study will be useful for impact studies, and ultimately in devising future policies for adaptation in the region.

Twenty-First-Century Snowfall and Snowpack Changes over the Southern California Mountains

2015 ◽  
Vol 29 (1) ◽  
pp. 91-110 ◽  
Author(s):  
Fengpeng Sun ◽  
Alex Hall ◽  
Marla Schwartz ◽  
Daniel B. Walton ◽  
Neil Berg
Keyword(s):  
General Circulation ◽  
Southern California ◽  
Snow Water Equivalent ◽  
Coupled Model ◽  
General Circulation Models ◽  
First Century ◽  
Climate Projections ◽  
Rcp Scenarios ◽  
Ensemble Mean ◽  
Twenty First Century

Abstract Future snowfall and snowpack changes over the mountains of Southern California are projected using a new hybrid dynamical–statistical framework. Output from all general circulation models (GCMs) in phase 5 of the Coupled Model Intercomparison Project archive is downscaled to 2-km resolution over the region. Variables pertaining to snow are analyzed for the middle (2041–60) and end (2081–2100) of the twenty-first century under two representative concentration pathway (RCP) scenarios: RCP8.5 (business as usual) and RCP2.6 (mitigation). These four sets of projections are compared with a baseline reconstruction of climate from 1981 to 2000. For both future time slices and scenarios, ensemble-mean total winter snowfall loss is widespread. By the mid-twenty-first century under RCP8.5, ensemble-mean winter snowfall is about 70% of baseline, whereas the corresponding value for RCP2.6 is somewhat higher (about 80% of baseline). By the end of the century, however, the two scenarios diverge significantly. Under RCP8.5, snowfall sees a dramatic further decline; 2081–2100 totals are only about half of baseline totals. Under RCP2.6, only a negligible further reduction from midcentury snowfall totals is seen. Because of the spread in the GCM climate projections, these figures are all associated with large intermodel uncertainty. Snowpack on the ground, as represented by 1 April snow water equivalent is also assessed. Because of enhanced snowmelt, the loss seen in snowpack is generally 50% greater than that seen in winter snowfall. By midcentury under RCP8.5, warming-accelerated spring snowmelt leads to snow-free dates that are about 1–3 weeks earlier than in the baseline period.

scholarly journals Quantifying the Uncertainty in Historical and Future Simulations of Northern Hemisphere Spring Snow Cover

2016 ◽  
Vol 29 (23) ◽  
pp. 8647-8663 ◽  
Author(s):  
Chad W. Thackeray ◽  
Christopher G. Fletcher ◽  
Lawrence R. Mudryk ◽  
Chris Derksen
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Climate Model ◽  
Coupled Model ◽  
Internal Variability ◽  
Winter Precipitation ◽  
First Century ◽  
Internal Climate Variability ◽  
Snow Cover Extent ◽  
Twenty First Century

Abstract Projections of twenty-first-century Northern Hemisphere (NH) spring snow cover extent (SCE) from two climate model ensembles are analyzed to characterize their uncertainty. Phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel ensemble exhibits variability resulting from both model differences and internal climate variability, whereas spread generated from a Canadian Earth System Model–Large Ensemble (CanESM-LE) experiment is solely a result of internal variability. The analysis shows that simulated 1981–2010 spring SCE trends are slightly weaker than observed (using an ensemble of snow products). Spring SCE is projected to decrease by −3.7% ± 1.1% decade−1 within the CMIP5 ensemble over the twenty-first century. SCE loss is projected to accelerate for all spring months over the twenty-first century, with the exception of June (because most snow in this month has melted by the latter half of the twenty-first century). For 30-yr spring SCE trends over the twenty-first century, internal variability estimated from CanESM-LE is substantial, but smaller than intermodel spread from CMIP5. Additionally, internal variability in NH extratropical land warming trends can affect SCE trends in the near future (R2 = 0.45), while variability in winter precipitation can also have a significant (but lesser) impact on SCE trends. On the other hand, a majority of the intermodel spread is driven by differences in simulated warming (dominant in March–May) and snow cover available for melt (dominant in June). The strong temperature–SCE linkage suggests that model uncertainty in projections of SCE could be potentially reduced through improved simulation of spring season warming over land.

scholarly journals The changing nature of hydroclimatic risks across South Africa

2021 ◽  
Vol 168 (3-4) ◽  
Author(s):  
Adam Schlosser ◽  
Andrei Sokolov ◽  
Ken Strzepek ◽  
Tim Thomas ◽  
Xiang Gao ◽  
...  
Keyword(s):  
South Africa ◽  
Air Temperature ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
First Century ◽  
Near Surface ◽  
Climate Targets ◽  
Twenty First Century

AbstractWe present results from large ensembles of projected twenty-first century changes in seasonal precipitation and near-surface air temperature for the nation of South Africa. These ensembles are a result of combining Monte Carlo projections from a human-Earth system model of intermediate complexity with pattern-scaled responses from climate models of the Coupled Model Intercomparison Project Phase 5 (CMIP5). These future ensemble scenarios consider a range of global actions to abate emissions through the twenty-first century. We evaluate distributions of surface-air temperature and precipitation change over three sub-national regions: western, central, and eastern South Africa. In all regions, we find that without any emissions or climate targets in place, there is a greater than 50% likelihood that mid-century temperatures will increase threefold over the current climate’s two-standard deviation range of variability. However, scenarios that consider more aggressive climate targets all but eliminate the risk of these salient temperature increases. A preponderance of risk toward decreased precipitation (3 to 4 times higher than increased) exists for western and central South Africa. Strong climate targets abate evolving regional hydroclimatic risks. Under a target to limit global climate warming to 1.5 °C by 2100, the risk of precipitation changes within South Africa toward the end of this century (2065–2074) is commensurate to the risk during the 2030s without any global climate target. Thus, these regional hydroclimate risks over South Africa could be delayed by 30 years and, in doing so, provide invaluable lead-time for national efforts to prepare, fortify, and/or adapt.

scholarly journals Surface Air Temperature Changes over the Twentieth and Twenty-First Centuries in China Simulated by 20 CMIP5 Models

2014 ◽  
Vol 27 (11) ◽  
pp. 3920-3937 ◽  
Author(s):  
Liang Chen ◽  
Oliver W. Frauenfeld
Keyword(s):  
Coupled Model ◽  
Surface Air Temperature ◽  
Earth System Model ◽  
First Century ◽  
System Model ◽  
The Tibetan Plateau ◽  
Earth System ◽  
Temperature Trends ◽  
Intercomparison Project ◽  
Twenty First Century

Abstract Historical temperature variability over China during the twentieth century and projected changes under three emission scenarios for the twenty-first century are evaluated on the basis of a multimodel ensemble of 20 GCMs from phase 5 of the Coupled Model Intercomparison Project (CMIP5) and two observational datasets. Changes relative to phase 3 of the Coupled Model Intercomparison Project (CMIP3) are assessed, and the performance of individual GCMs is also quantified. Compared with observations, GCMs have substantial cold biases over the Tibetan Plateau, especially in the cold season. The timing and location of these biases also correspond to the greatest disagreement among the individual models, indicating GCMs’ limitations in reproducing climatic features in this complex terrain. The CMIP5 multimodel ensemble shows better agreement with observations than CMIP3 in terms of the temperature biases. Both CMIP3 and CMIP5 capture the climatic warming over the twentieth century. However, the magnitude of the annual mean temperature trends is underestimated. There is also limited agreement in the spatial and seasonal patterns of temperature trends over China. Based on six statistical measures, four individual models—the Max Planck Institute Earth System Model, low resolution (MPI-ESM-LR), Second Generation Canadian Earth System Model (CanESM2), Model for Interdisciplinary Research on Climate, Earth System Model (MIROC-ESM), and Community Climate System Model, version 4 (CCSM4)—best represent surface air temperature variability over China. The future temperature projections indicate that the representative concentration pathway (RCP) 8.5 and RCP 4.5 scenarios exhibit a gradual increase in annual temperature during the twenty-first century at a rate of 0.60° and 0.27°C (10 yr)−1, respectively. As the lowest-emission mitigation scenario, RCP 2.6 projects the lowest rate of temperature increase [0.10°C (10 yr)−1]. By the end of the twenty-first century, temperature is projected to increase by 1.7°–5.7°C, with larger warming over northern China and the Tibetan Plateau.

scholarly journals Probability-Weighted Ensembles of U.S. County-Level Climate Projections for Climate Risk Analysis

2016 ◽  
Vol 55 (10) ◽  
pp. 2301-2322 ◽  
Author(s):  
D. J. Rasmussen ◽  
Malte Meinshausen ◽  
Robert E. Kopp
Keyword(s):  
United States ◽  
Joint Probability ◽  
Coupled Model ◽  
The United States ◽  
First Century ◽  
Climate Projections ◽  
Intergovernmental Panel ◽  
Temperature And Precipitation ◽  
Twenty First Century ◽  
Mixed Ensemble

AbstractQuantitative assessment of climate change risk requires a method for constructing probabilistic time series of changes in physical climate parameters. Here, two such methods, surrogate/model mixed ensemble (SMME) and Monte Carlo pattern/residual (MCPR), are developed and then are applied to construct joint probability density functions (PDFs) of temperature and precipitation change over the twenty-first century for every county in the United States. Both methods produce likely (67% probability) temperature and precipitation projections that are consistent with the Intergovernmental Panel on Climate Change’s interpretation of an equal-weighted Coupled Model Intercomparison Project phase 5 (CMIP5) ensemble but also provide full PDFs that include tail estimates. For example, both methods indicate that, under “Representative Concentration Pathway” 8.5, there is a 5% chance that the contiguous United States could warm by at least 8°C between 1981–2010 and 2080–99. Variance decomposition of SMME and MCPR projections indicates that background variability dominates uncertainty in the early twenty-first century whereas forcing-driven changes emerge in the second half of the twenty-first century. By separating CMIP5 projections into unforced and forced components using linear regression, these methods generate estimates of unforced variability from existing CMIP5 projections without requiring the computationally expensive use of multiple realizations of a single GCM.

scholarly journals Projected Changes in Temperature and Precipitation Extremes in China by the CMIP5 Multimodel Ensembles

2014 ◽  
Vol 27 (17) ◽  
pp. 6591-6611 ◽  
Author(s):  
Botao Zhou ◽  
Qiuzi Han Wen ◽  
Ying Xu ◽  
Lianchun Song ◽  
Xuebin Zhang
Keyword(s):  
Regional Scale ◽  
Coupled Model ◽  
First Century ◽  
Precipitation Extremes ◽  
Dominant Factor ◽  
Emission Scenarios ◽  
Temperature And Precipitation ◽  
Twenty First Century ◽  
The Difference ◽  
Projected Changes

Abstract This paper presents projected changes in temperature and precipitation extremes in China by the end of the twenty-first century based on the Coupled Model Intercomparison Project phase 5 (CMIP5) simulations. The temporal changes and their spatial patterns in the Expert Team on Climate Change Detection and Indices (ETCCDI) indices under the RCP4.5 and RCP8.5 emission scenarios are analyzed. Compared to the reference period 1986–2005, substantial changes are projected in temperature and precipitation extremes under both emission scenarios. These changes include a decrease in cold extremes, an increase in warm extremes, and an intensification of precipitation extremes. The intermodel spread in the projection increases with time, with wider spread under RCP8.5 than RCP4.5 for most indices, especially at the subregional scale. The difference in the projected changes under the two RCPs begins to emerge in the 2040s. Analyses based on the mixed-effects analysis of variance (ANOVA) model indicate that by the end of the twenty-first century, at the national scale, the dominant contributor to the projection uncertainty of most temperature-based indices, and some precipitation extremes [including maximum 1-day precipitation (RX1day) and maximum 5-day precipitation (RX5day), and total extremely wet day total amount (R95p)], is the difference in emission scenarios. By the end of the twenty-first century, model uncertainty is the dominant factor at the regional scale and for the other indices. Natural variability can also play very important role.

scholarly journals How Climate Change Affects Extremes in Maize and Wheat Yield in Two Cropping Regions

2015 ◽  
Vol 28 (12) ◽  
pp. 4653-4687 ◽  
Author(s):  
Caroline C. Ummenhofer ◽  
Hong Xu ◽  
Tracy E. Twine ◽  
Evan H. Girvetz ◽  
Heather R. McCarthy ◽  
...  
Keyword(s):  
Climate Model ◽  
Wheat Yield ◽  
Coupled Model ◽  
Growing Season ◽  
Maize Yield ◽  
First Century ◽  
Southeastern Australia ◽  
Marginal Areas ◽  
Twenty First Century ◽  
Yield Responses

Abstract Downscaled climate model projections from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were used to force a dynamic vegetation agricultural model (Agro-IBIS) and simulate yield responses to historical climate and two future emissions scenarios for maize in the U.S. Midwest and wheat in southeastern Australia. In addition to mean changes in yield, the frequency of high- and low-yield years was related to changing local hydroclimatic conditions. Particular emphasis was on the seasonal cycle of climatic variables during extreme-yield years and links to crop growth. While historically high (low) yields in Iowa tend to occur during years with anomalous wet (dry) growing season, this is exacerbated in the future. By the end of the twenty-first century, the multimodel mean (MMM) of growing season temperatures in Iowa is projected to increase by more than 5°C, and maize yield is projected to decrease by 18%. For southeastern Australia, the frequency of low-yield years rises dramatically in the twenty-first century because of significant projected drying during the growing season. By the late twenty-first century, MMM growing season precipitation in southeastern Australia is projected to decrease by 15%, temperatures are projected to increase by 2.8°–4.5°C, and wheat yields are projected to decline by 70%. Results highlight the sensitivity of yield projections to the nature of hydroclimatic changes. Where future changes are uncertain, the sign of the yield change simulated by Agro-IBIS is uncertain as well. In contrast, broad agreement in projected drying over southern Australia across models is reflected in consistent yield decreases for the twenty-first century. Climatic changes of the order projected can be expected to pose serious challenges for continued staple grain production in some current centers of production, especially in marginal areas.

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