scholarly journals Mid-to-late Holocene temperature evolution and atmospheric dynamics over Europe in regional model simulations

2016 ◽  
Vol 12 (8) ◽  
pp. 1645-1662 ◽  
Author(s):  
Emmanuele Russo ◽  
Ulrich Cubasch
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Late Holocene ◽  
Climate Models ◽  
Climate Model ◽  
Set Covering ◽  
Data Set ◽  
Near Surface ◽  
Past Climate ◽  
Past Climate Change

Abstract. The improvement in resolution of climate models has always been mentioned as one of the most important factors when investigating past climatic conditions, especially in order to evaluate and compare the results against proxy data. Despite this, only a few studies have tried to directly estimate the possible advantages of highly resolved simulations for the study of past climate change. Motivated by such considerations, in this paper we present a set of high-resolution simulations for different time slices of the mid-to-late Holocene performed over Europe using the state-of-the-art regional climate model COSMO-CLM. After proposing and testing a model configuration suitable for paleoclimate applications, the aforementioned mid-to-late Holocene simulations are compared against a new pollen-based climate reconstruction data set, covering almost all of Europe, with two main objectives: testing the advantages of high-resolution simulations for paleoclimatic applications, and investigating the response of temperature to variations in the seasonal cycle of insolation during the mid-to-late Holocene. With the aim of giving physically plausible interpretations of the mismatches between model and reconstructions, possible uncertainties of the pollen-based reconstructions are taken into consideration. Focusing our analysis on near-surface temperature, we can demonstrate that concrete advantages arise in the use of highly resolved data for the comparison against proxy-reconstructions and the investigation of past climate change. Additionally, our results reinforce previous findings showing that summertime temperatures during the mid-to-late Holocene were driven mainly by changes in insolation and that the model is too sensitive to such changes over Southern Europe, resulting in drier and warmer conditions. However, in winter, the model does not correctly reproduce the same amplitude of changes evident in the reconstructions, even if it captures the main pattern of the pollen data set over most of the domain for the time periods under investigation. Through the analysis of variations in atmospheric circulation we suggest that, even though the wintertime discrepancies between the two data sets in some areas are most likely due to high pollen uncertainties, in general the model seems to underestimate the changes in the amplitude of the North Atlantic Oscillation, overestimating the contribution of secondary modes of variability.

scholarly journals Acquiring High to Ultra-High Resolution Geological Records of Past Climate Change by Scientific Drilling

2009 ◽  
Vol 8 ◽  
pp. 46-56 ◽  
Author(s):  
J. Thurow ◽  
L. C. Peterson ◽  
U. Harms ◽  
D. A. Hodell ◽  
H. Cheshire ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Past Climate ◽  
Scientific Drilling ◽  
Past Climate Change

No abstract available. <br><br> doi:<a href="http://dx.doi.org/10.2204/iodp.sd.8.08.2009" target="_blank">10.2204/iodp.sd.8.08.2009</a>

scholarly journals Regional Climate Model Emulator based on Deep Learning: Concept and First Evaluation of a Novel Hybrid Downscaling Approach

2021 ◽  
Author(s):  
Antoine Doury ◽  
Samuel Somot ◽  
Sébastien Gadat ◽  
Aurélien Ribes ◽  
Lola Corre
Keyword(s):  
Climate Change ◽  
Network Architecture ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate ◽  
Near Surface ◽  
The Way

Abstract Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). The aim of this tool is to enlarge the size of high-resolution RCM simulation ensembles at low cost.We build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. Furthermore, the emulator relies on a neural network architecture, which grants computational efficiency. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM and in particular the way the RCM refines locally the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a huge computational benefit in running the emulator rather than the RCM, since training the emulator takes about 2 hours on GPU, and the prediction is nearly instantaneous. However, further work is needed to improve the way the RCM-emulator reproduces some of the temperature extremes, the intensity of climate change, and to extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest.

scholarly journals Quantifying the Likelihood of Regional Climate Change: A Hybridized Approach

2013 ◽  
Vol 26 (10) ◽  
pp. 3394-3414 ◽  
Author(s):  
C. Adam Schlosser ◽  
Xiang Gao ◽  
Kenneth Strzepek ◽  
Andrei Sokolov ◽  
Chris E. Forest ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Climate Projections ◽  
Massachusetts Institute Of Technology ◽  
Adaptation To Climate Change ◽  
Intergovernmental Panel ◽  
Near Surface ◽  
Institute Of Technology

Abstract The growing need for risk-based assessments of impacts and adaptation to climate change calls for increased capability in climate projections: specifically, the quantification of the likelihood of regional outcomes and the representation of their uncertainty. Herein, the authors present a technique that extends the latitudinal projections of the 2D atmospheric model of the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM) by applying longitudinally resolved patterns from observations, and from climate model projections archived from exercises carried out for the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC). The method maps the IGSM zonal means across longitude using a set of transformation coefficients, and this approach is demonstrated in application to near-surface air temperature and precipitation, for which high-quality observational datasets and model simulations of climate change are available. The current climatology of the transformation coefficients is observationally based. To estimate how these coefficients may alter with climate, the authors characterize the climate models’ spatial responses, relative to their zonal mean, from transient increases in trace-gas concentrations and then normalize these responses against their corresponding transient global temperature responses. This procedure allows for the construction of metaensembles of regional climate outcomes, combining the ensembles of the MIT IGSM—which produce global and latitudinal climate projections, with uncertainty, under different global climate policy scenarios—with regionally resolved patterns from the archived IPCC climate model projections. This hybridization of the climate model longitudinal projections with the global and latitudinal patterns projected by the IGSM can, in principle, be applied to any given state or flux variable that has the sufficient observational and model-based information.

scholarly journals Local impact analysis of climate change on precipitation extremes: are high-resolution climate models needed for realistic simulations?

2016 ◽  
Author(s):  
Hossein Tabari ◽  
Rozemien De Troch ◽  
Olivier Giot ◽  
Rafiq Hamdi ◽  
Piet Termonia ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Spatial Resolution ◽  
Impact Analysis ◽  
Climate Models ◽  
Climate Model ◽  
Daily Precipitation ◽  
Reanalysis Data ◽  
Precipitation Extremes ◽  
Added Value

Abstract. This study explores whether climate models with higher spatial resolution provide higher accuracy for precipitation simulations and/or different climate change signals. The outputs from two convection-permitting climate models (ALARO and CCLM) with a spatial resolution of 3–4 km are compared with those from the coarse scale driving models or reanalysis data for simulating/projecting daily and sub-daily precipitation quantiles. The high-resolution ALARO and CCLM models reveal an added value to capture sub-daily precipitation extremes during summer compared to the driving GCMs and reanalysis data. Further validation of historical climate simulations based on design precipitation statistics derived from intensity–duration–frequency (IDF) curves shows a better match of the convection-permitting model results with the observations-based IDF statistics. Results moreover indicate that one has to be careful in assuming spatial scale independency of climate change signals for the delta change downscaling method, as high-resolution models may show larger changes in extreme precipitation. These larger changes appear to be dependent on the climate model, since such intensification is not observed for the ALARO model.

scholarly journals Modelling the climate and surface mass balance of polar ice sheets using RACMO2 – Part 2: Antarctica (1979–2016)

2018 ◽  
Vol 12 (4) ◽  
pp. 1479-1498 ◽  
Author(s):  
Jan Melchior van Wessem ◽  
Willem Jan van de Berg ◽  
Brice P. Y. Noël ◽  
Erik van Meijgaard ◽  
Charles Amory ◽  
...  
Keyword(s):  
High Resolution ◽  
Mass Balance ◽  
Climate Model ◽  
Ice Sheet ◽  
Surface Mass Balance ◽  
Data Set ◽  
Near Surface ◽  
Surface Mass ◽  
Model Version ◽  
Surface Climate

Abstract. We evaluate modelled Antarctic ice sheet (AIS) near-surface climate, surface mass balance (SMB) and surface energy balance (SEB) from the updated polar version of the regional atmospheric climate model, RACMO2 (1979–2016). The updated model, referred to as RACMO2.3p2, incorporates upper-air relaxation, a revised topography, tuned parameters in the cloud scheme to generate more precipitation towards the AIS interior and modified snow properties reducing drifting snow sublimation and increasing surface snowmelt. Comparisons of RACMO2 model output with several independent observational data show that the existing biases in AIS temperature, radiative fluxes and SMB components are further reduced with respect to the previous model version. The model-integrated annual average SMB for the ice sheet including ice shelves (minus the Antarctic Peninsula, AP) now amounts to 2229 Gt y−1, with an interannual variability of 109 Gt y−1. The largest improvement is found in modelled surface snowmelt, which now compares well with satellite and weather station observations. For the high-resolution (∼ 5.5 km) AP simulation, results remain comparable to earlier studies. The updated model provides a new, high-resolution data set of the contemporary near-surface climate and SMB of the AIS; this model version will be used for future climate scenario projections in a forthcoming study.

scholarly journals Probabilistic evaluation of competing climate models

2017 ◽  
Vol 3 (2) ◽  
pp. 93-105 ◽  
Author(s):  
Amy Braverman ◽  
Snigdhansu Chatterjee ◽  
Megan Heyman ◽  
Noel Cressie
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Parametric Bootstrap ◽  
Summary Statistics ◽  
Model Output ◽  
Data Set ◽  
Near Surface ◽  
Climate Model Simulations ◽  
Original Time ◽  
Time Sequences

Abstract. Climate models produce output over decades or longer at high spatial and temporal resolution. Starting values, boundary conditions, greenhouse gas emissions, and so forth make the climate model an uncertain representation of the climate system. A standard paradigm for assessing the quality of climate model simulations is to compare what these models produce for past and present time periods, to observations of the past and present. Many of these comparisons are based on simple summary statistics called metrics. In this article, we propose an alternative: evaluation of competing climate models through probabilities derived from tests of the hypothesis that climate-model-simulated and observed time sequences share common climate-scale signals. The probabilities are based on the behavior of summary statistics of climate model output and observational data over ensembles of pseudo-realizations. These are obtained by partitioning the original time sequences into signal and noise components, and using a parametric bootstrap to create pseudo-realizations of the noise sequences. The statistics we choose come from working in the space of decorrelated and dimension-reduced wavelet coefficients. Here, we compare monthly sequences of CMIP5 model output of average global near-surface temperature anomalies to similar sequences obtained from the well-known HadCRUT4 data set as an illustration.

Simulated Climate and Climate Change in the GFDL CM2.5 High-Resolution Coupled Climate Model

2012 ◽  
Vol 25 (8) ◽  
pp. 2755-2781 ◽  
Author(s):  
Thomas L. Delworth ◽  
Anthony Rosati ◽  
Whit Anderson ◽  
Alistair J. Adcroft ◽  
V. Balaji ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Climate Model ◽  
Global Climate Model ◽  
Atmospheric Co2 ◽  
Geophysical Fluid Dynamics Laboratory ◽  
High Latitudes ◽  
Near Surface ◽  
Simulated Climate ◽  
The Tropics

Abstract The authors present results for simulated climate and climate change from a newly developed high-resolution global climate model [Geophysical Fluid Dynamics Laboratory Climate Model version 2.5 (GFDL CM2.5)]. The GFDL CM2.5 has an atmospheric resolution of approximately 50 km in the horizontal, with 32 vertical levels. The horizontal resolution in the ocean ranges from 28 km in the tropics to 8 km at high latitudes, with 50 vertical levels. This resolution allows the explicit simulation of some mesoscale eddies in the ocean, particularly at lower latitudes. Analyses are presented based on the output of a 280-yr control simulation; also presented are results based on a 140-yr simulation in which atmospheric CO2 increases at 1% yr−1 until doubling after 70 yr. Results are compared to GFDL CM2.1, which has somewhat similar physics but a coarser resolution. The simulated climate in CM2.5 shows marked improvement over many regions, especially the tropics, including a reduction in the double ITCZ and an improved simulation of ENSO. Regional precipitation features are much improved. The Indian monsoon and Amazonian rainfall are also substantially more realistic in CM2.5. The response of CM2.5 to a doubling of atmospheric CO2 has many features in common with CM2.1, with some notable differences. For example, rainfall changes over the Mediterranean appear to be tightly linked to topography in CM2.5, in contrast to CM2.1 where the response is more spatially homogeneous. In addition, in CM2.5 the near-surface ocean warms substantially in the high latitudes of the Southern Ocean, in contrast to simulations using CM2.1.

High-Resolution Climate Change Impact Analysis on Expected Future Water Availability in the Upper Jordan Catchment and the Middle East

2014 ◽  
Vol 15 (4) ◽  
pp. 1517-1531 ◽  
Author(s):  
Gerhard Smiatek ◽  
Harald Kunstmann ◽  
Andreas Heckl
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Water Availability ◽  
Impact Analysis ◽  
River Flow ◽  
Climate Model ◽  
Mesoscale Model ◽  
Global Climate Model ◽  
Full Range ◽  
The Impact

Abstract The impact of climate change on the future water availability of the upper Jordan River (UJR) and its tributaries Dan, Snir, and Hermon located in the eastern Mediterranean is evaluated by a highly resolved distributed approach with the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) run at 18.6- and 6.2-km resolution offline coupled with the Water Flow and Balance Simulation Model (WaSiM). The MM5 was driven with NCEP reanalysis for 1971–2000 and with Hadley Centre Coupled Model, version 3 (HadCM3), GCM forcings for 1971–2099. Because only one regional–global climate model combination was applied, the results may not give the full range of possible future projections. To describe the Dan spring behavior, the hydrological model was extended by a bypass approach to allow the fast discharge components of the Snir to enter the Dan catchment. Simulation results for the period 1976–2000 reveal that the coupled system was able to reproduce the observed discharge rates in the partially karstic complex terrain to a reasonable extent with the high-resolution 6.2-km meteorological input only. The performed future climate simulations show steadily rising temperatures with 2.2 K above the 1976–2000 mean for the period 2031–60 and 3.5 K for the period 2070–99. Precipitation trends are insignificant until the middle of the century, although a decrease of approximately 12% is simulated. For the end of the century, a reduction in rainfall ranging between 10% and 35% can be expected. Discharge in the UJR is simulated to decrease by 12% until 2060 and by 26% until 2099, both related to the 1976–2000 mean. The discharge decrease is associated with a lower number of high river flow years.

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