Back to the Future: Using Long-Term Observational and Paleo-Proxy Reconstructions to Improve Model Projections of Antarctic Climate

Quantitative estimates of future Antarctic climate change are derived from numerical global climate models. Evaluation of the reliability of climate model projections involves many lines of evidence on past performance combined with knowledge of the processes that need to be represented. Routine model evaluation is mainly based on the modern observational period, which started with the establishment of a network of Antarctic weather stations in 1957/58. This period is too short to evaluate many fundamental aspects of the Antarctic and Southern Ocean climate system, such as decadal-to-century time-scale climate variability and trends. To help address this gap, we present a new evaluation of potential ways in which long-term observational and paleo-proxy reconstructions may be used, with a particular focus on improving projections. A wide range of data sources and time periods is included, ranging from ship observations of the early 20th century to ice core records spanning hundreds to hundreds of thousands of years to sediment records dating back 34 million years. We conclude that paleo-proxy records and long-term observational datasets are an underused resource in terms of strategies for improving Antarctic climate projections for the 21st century and beyond. We identify priorities and suggest next steps to addressing this.

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Blue Water in Europe: Estimates of Current and Future Availability and Analysis of Uncertainty

Water ◽

10.3390/w11030420 ◽

2019 ◽

Vol 11 (3) ◽

pp. 420 ◽

Cited By ~ 5

Author(s):

Alvaro Sordo-Ward ◽

Isabel Granados ◽

Ana Iglesias ◽

Luis Garrote

Keyword(s):

High Resolution ◽

Water Availability ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Baseline Period ◽

Global Climate Models ◽

Blue Water ◽

Large Area

This study presents a regional assessment of future blue water availability in Europe under different assumptions. The baseline period (1960 to 1999) is compared to the near future (2020 to 2059) and the long-term future (2060 to 2099). Blue water availability is estimated as the maximum amount of water supplied at a certain point of the river network that satisfies a defined demand, taking into account specified reliability requirements. Water availability is computed with the geospatial high-resolution Water Availability and Adaptation Policy Assessment (WAAPA) model. The WAAPA model definition for this study extends over 6 million km2 in Europe and considers almost 4000 sub-basins in Europe. The model takes into account 2300 reservoirs larger than 5 hm3, and the dataset of Hydro 1k with 1700 sub-basins. Hydrological scenarios for this study were taken from the Inter-Sectoral Impact Model Inter-Comparison Project and included simulations of five global climate models under different Representative Concentration Pathways scenarios. The choice of method is useful for evaluating large area regional studies that include high resolution on the systems´ characterization. The results highlight large uncertainties associated with a set of local water availability estimates across Europe. Climate model uncertainties for mean annual runoff and potential water availability were found to be higher than scenario uncertainties. Furthermore, the existing hydraulic infrastructure and its management have played an important role by decoupling water availability from hydrologic variability. This is observed for all climate models, the emissions scenarios considered, and for near and long-term future. The balance between water availability and withdrawals is threatened in some regions, such as the Mediterranean region. The results of this study contribute to defining potential challenges in water resource systems and regional risk areas.

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Climate Change Impact Assessment for Aji Basin Using Statistical Downscaling and Bias Correction of Climate Model Outputs

Current World Environment ◽

10.12944/cwe.11.2.40 ◽

2016 ◽

Vol 11 (2) ◽

pp. 670-678 ◽

Cited By ~ 1

Author(s):

N. S Vithlani ◽

H. D Rank

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Regional Climate ◽

Climate Extremes ◽

Global Climate Models ◽

Climate Indices ◽

Climate Projections

For the future projections Global climate models (GCMs) enable development of climate projections and relate greenhouse gas forcing to future potential climate states. When focusing it on smaller scales it exhibit some limitations to overcome this problem, regional climate models (RCMs) and other downscaling methods have been developed. To ensure statistics of the downscaled output matched the corresponding statistics of the observed data, bias correction was used. Quantify future changes of climate extremes were analyzed, based on these downscaled data from two RCMs grid points. Subset of indices and models, results of bias corrected model output and raw for the present day climate were compared with observation, which demonstrated that bias correction is important for RCM outputs. Bias correction directed agreements of extreme climate indices for future climate it does not correct for lag inverse autocorrelation and fraction of wet and dry days. But, it was observed that adjusting both the biases in the mean and variability, relatively simple non-linear correction, leads to better reproduction of observed extreme daily and multi-daily precipitation amounts. Due to climate change temperature and precipitation will increased day by day.

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Balanced estimate and uncertainty assessment of European climate change using the large EURO-CORDEX regional climate model ensemble

10.5194/esd-2021-8 ◽

2021 ◽

Author(s):

Guillaume Evin ◽

Samuel Somot ◽

Benoit Hingray

Keyword(s):

Regional Climate Model ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Regional Climate ◽

Uncertainty Assessment ◽

Global Climate Models ◽

Climate Projections ◽

Total Uncertainty ◽

Near Surface

Abstract. Large Multiscenarios Multimodel Ensembles (MMEs) of regional climate model (RCM) experiments driven by Global Climate Models (GCM) are made available worldwide and aim at providing robust estimates of climate changes and associated uncertainties. Due to many missing combinations of emission scenarios and climate models leading to sparse Scenario-GCM-RCM matrices, these large ensembles are however very unbalanced, which makes uncertainty analyses impossible with standard approaches. In this paper, the uncertainty assessment is carried out by applying an advanced statistical approach, called QUALYPSO, to a very large ensemble of 87 EURO-CORDEX climate projections, the largest ensemble ever produced for regional projections in Europe. This analysis provides i) the most up-to-date and balanced estimates of mean changes for near-surface temperature and precipitation in Europe, ii) the total uncertainty of projections and its partition as a function of time, and iii) the list of the most important contributors to the model uncertainty. For changes of total precipitation and mean temperature in winter (DJF) and summer (JJA), the uncertainty due to RCMs can be as large as the uncertainty due to GCMs at the end of the century (2071–2099). Both uncertainty sources are mainly due to a small number of individual models clearly identified. Due to the highly unbalanced character of the MME, mean estimated changes can drastically differ from standard average estimates based on the raw ensemble of opportunity. For the RCP4.5 emission scenario in Central-Eastern Europe for instance, the difference between balanced and direct estimates are up to 0.8 °C for summer temperature changes and up to 20 % for summer precipitation changes at the end of the century.

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Connecting Climate Model Projections of Global Temperature Change with the Real World

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-14-00154.1 ◽

2016 ◽

Vol 97 (6) ◽

pp. 963-980 ◽

Cited By ~ 39

Author(s):

Ed Hawkins ◽

Rowan Sutton

Keyword(s):

Climate Models ◽

Climate Model ◽

Global Climate ◽

Coupled Model ◽

Global Climate Models ◽

Global Temperature ◽

Climate Projections ◽

Reference Period ◽

Intergovernmental Panel ◽

Temperature Levels

Abstract Current state-of-the-art global climate models produce different values for Earth’s mean temperature. When comparing simulations with each other and with observations, it is standard practice to compare temperature anomalies with respect to a reference period. It is not always appreciated that the choice of reference period can affect conclusions, both about the skill of simulations of past climate and about the magnitude of expected future changes in climate. For example, observed global temperatures over the past decade are toward the lower end of the range of the phase 5 of the Coupled Model Intercomparison Project (CMIP5) simulations irrespective of what reference period is used, but exactly where they lie in the model distribution varies with the choice of reference period. Additionally, we demonstrate that projections of when particular temperature levels are reached, for example, 2 K above “preindustrial,” change by up to a decade depending on the choice of reference period. In this article, we discuss some of the key issues that arise when using anomalies relative to a reference period to generate climate projections. We highlight that there is no perfect choice of reference period. When evaluating models against observations, a long reference period should generally be used, but how long depends on the quality of the observations available. The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) choice to use a 1986–2005 reference period for future global temperature projections was reasonable, but a case-by-case approach is needed for different purposes and when assessing projections of different climate variables. Finally, we recommend that any studies that involve the use of a reference period should explicitly examine the robustness of the conclusions to alternative choices.

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Multivariate Estimations of Equilibrium Climate Sensitivity from Short Transient Warming Simulations

10.5194/egusphere-egu21-187 ◽

2021 ◽

Author(s):

Robbin Bastiaansen ◽

Henk Dijkstra ◽

Anna von der Heydt

Keyword(s):

Time Series ◽

Climate Sensitivity ◽

Climate Models ◽

Global Climate ◽

Equilibrium Temperature ◽

Global Climate Models ◽

Equilibrium Climate Sensitivity ◽

Wide Range ◽

Short Time

<p>One of the most used metrics to gauge the effects of climate change is the equilibrium climate sensitivity, defined as the long-term (equilibrium) temperature increase resulting from instantaneous doubling of atmospheric CO2. Since global climate models cannot be fully equilibrated in practice, extrapolation techniques are used to estimate the equilibrium state from transient warming simulations. Because of the abundance of climate feedbacks &#8211; spanning a wide range of temporal scales &#8211; it is hard to extract long-term behaviour from short-time series; predominantly used techniques are only capable of detecting the single most dominant eigenmode, thus hampering their ability to give accurate long-term estimates. Here, we present an extension to those methods by incorporating data from multiple observables in a multi-component linear regression model. This way, not only the dominant but also the next-dominant eigenmodes of the climate system are captured, leading to better long-term estimates from short, non-equilibrated time series.</p>

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Balanced estimate and uncertainty assessment of European climate change using the large EURO-CORDEX regional climate model ensemble

Earth System Dynamics ◽

10.5194/esd-12-1543-2021 ◽

2021 ◽

Vol 12 (4) ◽

pp. 1543-1569

Author(s):

Guillaume Evin ◽

Samuel Somot ◽

Benoit Hingray

Keyword(s):

Regional Climate Model ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Regional Climate ◽

Uncertainty Assessment ◽

Global Climate Models ◽

Climate Projections ◽

Total Uncertainty ◽

Near Surface

Abstract. Large multiscenario multimodel ensembles (MMEs) of regional climate model (RCM) experiments driven by global climate models (GCMs) are made available worldwide and aim at providing robust estimates of climate changes and associated uncertainties. Due to many missing combinations of emission scenarios and climate models leading to sparse scenario–GCM–RCM matrices, these large ensembles, however, are very unbalanced, which makes uncertainty analyses impossible with standard approaches. In this paper, the uncertainty assessment is carried out by applying an advanced statistical approach, called QUALYPSO, to a very large ensemble of 87 EURO-CORDEX climate projections, the largest MME based on regional climate models ever produced in Europe. This analysis provides a detailed description of this MME, including (i) balanced estimates of mean changes for near-surface temperature and precipitation in Europe, (ii) the total uncertainty of projections and its partition as a function of time, and (iii) the list of the most important contributors to the model uncertainty. For changes in total precipitation and mean temperature in winter (DJF) and summer (JJA), the uncertainty due to RCMs can be as large as the uncertainty due to GCMs at the end of the century (2071–2099). Both uncertainty sources are mainly due to a small number of individual models clearly identified. Due to the highly unbalanced character of the MME, mean estimated changes can drastically differ from standard average estimates based on the raw ensemble of opportunity. For the RCP4.5 emission scenario in central–eastern Europe for instance, the difference between balanced and direct estimates is up to 0.8 ∘C for summer temperature changes and up to 20 % for summer precipitation changes at the end of the century.

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UNCERTAINTY OF CLIMATE PROJECTIONS AND AN APPROACH UTILIZING CLIMATE MODEL OUTPUTS FOR HYDROLOGIC COMPUTATION IN THE BA RIVER BASIN

Vietnam Journal of Science and Technology ◽

10.15625/2525-2518/56/6/12663 ◽

2018 ◽

Vol 56 (6) ◽

pp. 732

Author(s):

Anh Thi Van Vu ◽

Thuc Tran ◽

Minh Truong Ha ◽

Lanh Thi Minh Pham

Keyword(s):

Climate Change ◽

River Basin ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Climate Change Impacts ◽

Global Climate Models ◽

Climate Projections ◽

Top Down ◽

Basin Scale

A top-down approach begins with Global Climate Models (GCMs) is a common method for assessing climate change impacts on water resources in river basins. To overcome the coarse resolution of GCMs, dynamic downscaling by regional climate models (RCMs) with bias-correction procedures is utilized with the aim to reflect the meteorological features at the river basin scale. However, the results still entail large uncertainties. This paper examines the ability to capture the observed baseline temperature and precipitation (1986-2005) in the Ba River Basin from GCM outputs, RCM outputs, bias-corrected GCM outputs and bias-corrected RCM outputs by analyzing statistical indicators between historical simulations and observed data in 4 temperature and 6 rainfall stations. Bias-corrected results of both GCM and RCM have significantly smaller errors compared to the unbias-corrected ones. The uncertainty of future climate projection for the mid and late 21th century of the bias-corrected GCMs and RCMs are evaluated. It is found that there is still uncertainty in projected results. A concept of “Decision-Scaling” which combines top-down and bottom-up approaches is proposed to assess the climate change impacts on hydrological system to take into account uncertainties of climate projections by models.

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Probability Assessments of an Ice-Free Arctic: Comparing Statistical and Climate Model Projections

10.5194/egusphere-egu2020-3717 ◽

2020 ◽

Author(s):

Glenn Rudebusch ◽

Francis Diebold

Keyword(s):

Sea Ice ◽

Climate Models ◽

Climate Model ◽

Global Climate ◽

Arctic Sea Ice ◽

Global Climate Models ◽

Economic Consequences ◽

Sea Ice Extent ◽

Wide Range ◽

Arctic Sea

<p>The downward trend in the amount of Arctic sea ice is a key factor determining the pace and intensity of future global climate change. Diminished sea ice also has a wide range of other environmental and economic consequences. Based on several decades of satellite data, we provide statistical forecasts of Arctic sea ice extent during the rest of this century. The best fitting statistical model indicates that overall sea ice coverage is declining at an increasing rate. By contrast, average projections from the CMIP5 global climate models foresee a gradual slowing of Arctic sea ice loss even in scenarios with high carbon emissions. Our long-range statistical projections also deliver probability assessments of the timing of an ice-free Arctic. These results indicate almost a 60 percent chance of an effectively ice-free Arctic Ocean during some summer in the 2030s -- much earlier than the average projection from global climate models.</p>

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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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The MJO-QBO Relationship in a GCM with Stratospheric Nudging

Journal of Climate ◽

10.1175/jcli-d-20-0636.1 ◽

2021 ◽

pp. 1-69

Author(s):

Zane Martin ◽

Clara Orbe ◽

Shuguang Wang ◽

Adam Sobel

Keyword(s):

Climate Models ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Global Climate Models ◽

Boreal Winter ◽

Quasi Biennial Oscillation ◽

Meridional Winds ◽

Goddard Institute ◽

Minimal Bias

AbstractObservational studies show a strong connection between the intraseasonal Madden-Julian oscillation (MJO) and the stratospheric quasi-biennial oscillation (QBO): the boreal winter MJO is stronger, more predictable, and has different teleconnections when the QBO in the lower stratosphere is easterly versus westerly. Despite the strength of the observed connection, global climate models do not produce an MJO-QBO link. Here the authors use a current-generation ocean-atmosphere coupled NASA Goddard Institute for Space Studies global climate model (Model E2.1) to examine the MJO-QBO link. To represent the QBO with minimal bias, the model zonal mean stratospheric zonal and meridional winds are relaxed to reanalysis fields from 1980-2017. The model troposphere, including the MJO, is allowed to freely evolve. The model with stratospheric nudging captures QBO signals well, including QBO temperature anomalies. However, an ensemble of nudged simulations still lacks an MJO-QBO connection.

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