Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA 1.0) compatible with CMIP climate data

Abstract. Tropical cyclones are responsible for a large share of global damage resulting from natural disasters and estimating cyclone-related damage at a national level is a challenge attracting growing interest in the context of climate change. The global climate models, whose outputs are available from the Coupled Model Intercomparison Project (CMIP), do not resolve tropical cyclones. The Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA) presented in this paper, couples statistical and thermodynamic relationships to generate synthetic tracks sensitive to local climate conditions and estimates the damage induced by tropical cyclones at a national level. The framework is designed to be compatible with CMIP models’ data offering a simple solution to resolve tropical cyclones in climate projections. We illustrate it by producing damage projections in Representative Concentration Pathways (RCP) at the global level and for individual countries. The algorithm contains a module to correct biases in climate models based on the distributions of the climate variables in the reanalyses.

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What Do Global Climate Models Tell Us about Future Arctic Sea Ice Coverage Changes?

Climate ◽

10.3390/cli8010015 ◽

2020 ◽

Vol 8 (1) ◽

pp. 15 ◽

Cited By ~ 1

Author(s):

Ge Peng ◽

Jessica L. Matthews ◽

Muyin Wang ◽

Russell Vose ◽

Liqiang Sun

Keyword(s):

Sea Ice ◽

Climate Models ◽

Climate Adaptation ◽

Global Climate ◽

Arctic Sea Ice ◽

Global Climate Models ◽

Climate Projections ◽

Emission Scenarios ◽

Ice Coverage ◽

Arctic Sea

The prospect of an ice-free Arctic in our near future due to the rapid and accelerated Arctic sea ice decline has brought about the urgent need for reliable projections of the first ice-free Arctic summer year (FIASY). Together with up-to-date observations and characterizations of Arctic ice state, they are essential to business strategic planning, climate adaptation, and risk mitigation. In this study, the monthly Arctic sea ice extents from 12 global climate models are utilized to obtain projected FIASYs and their dependency on different emission scenarios, as well as to examine the nature of the ice retreat projections. The average value of model-projected FIASYs is 2054/2042, with a spread of 74/42 years for the medium/high emission scenarios, respectively. The earliest FIASY is projected to occur in year 2023, which may not be realistic, for both scenarios. The sensitivity of individual climate models to scenarios in projecting FIASYs is very model-dependent. The nature of model-projected Arctic sea ice coverage changes is shown to be primarily linear. FIASY values predicted by six commonly used statistical models that were curve-fitted with the first 30 years of climate projections (2006–2035), on other hand, show a preferred range of 2030–2040, with a distinct peak at 2034 for both scenarios, which is more comparable with those from previous studies.

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Visual representation of CORDEX climate data by using QGIS

10.5194/egusphere-egu21-14599 ◽

2021 ◽

Author(s):

Kalamkas Yessimkhanova ◽

Mátyás Gede

Keyword(s):

Climate Models ◽

Data File ◽

Climate Projections ◽

Climate Data ◽

Local Climate ◽

Information Preference ◽

Time Period ◽

Open Source Gis ◽

Visualization Toolkit ◽

Spatial Referencing

The majority of studies are dedicated to the analysis of climate change and climate models with no regard for data visualization part. Therefore, this research is aimed at highlighting challenges, with an emphasis on spatial referencing that can occur while visualizing CORDEX data. CORDEX data are stored in NetCDF file format, and sometimes georeferencing may be misconceived in QGIS software. For this reason, two techniques of georeferencing data are examined in this work. The first way of data georeferencing is re-projecting coordinates from original projection to an interpolated latitude/longitude grid. The second way is re-encrypting initial data file so that QGIS is able to interpret projection information. Preference of using QGIS explained by two reasons: it is open source GIS application and it has expanded visualization toolkit.In addition, there are a great deal of climate models based on CORDEX data for some regions whereas there is a lack of climate projections for particular areas. In this regard, carrying out analysis for the region of Kazakhstan is beneficial. Outcomes of this research may stimulate spreading local climate models for Kazakhstan territory. Results are represented in the form of maps of Kazakhstan illustrating temperature change over 21st century time period.

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CMIP5 model selection for ISMIP6 ice sheet model forcing: Greenland and Antarctica

The Cryosphere ◽

10.5194/tc-14-855-2020 ◽

2020 ◽

Vol 14 (3) ◽

pp. 855-879 ◽

Cited By ~ 10

Author(s):

Alice Barthel ◽

Cécile Agosta ◽

Christopher M. Little ◽

Tore Hattermann ◽

Nicolas C. Jourdain ◽

...

Keyword(s):

Model Selection ◽

Climate Models ◽

Climate Modeling ◽

Global Climate ◽

Ice Sheet ◽

Global Climate Models ◽

Historical Period ◽

Climate Projections ◽

Subsurface Ocean ◽

Spatial Domains

Abstract. The ice sheet model intercomparison project for CMIP6 (ISMIP6) effort brings together the ice sheet and climate modeling communities to gain understanding of the ice sheet contribution to sea level rise. ISMIP6 conducts stand-alone ice sheet experiments that use space- and time-varying forcing derived from atmosphere–ocean coupled global climate models (AOGCMs) to reflect plausible trajectories for climate projections. The goal of this study is to recommend a subset of CMIP5 AOGCMs (three core and three targeted) to produce forcing for ISMIP6 stand-alone ice sheet simulations, based on (i) their representation of current climate near Antarctica and Greenland relative to observations and (ii) their ability to sample a diversity of projected atmosphere and ocean changes over the 21st century. The selection is performed separately for Greenland and Antarctica. Model evaluation over the historical period focuses on variables used to generate ice sheet forcing. For stage (i), we combine metrics of atmosphere and surface ocean state (annual- and seasonal-mean variables over large spatial domains) with metrics of time-mean subsurface ocean temperature biases averaged over sectors of the continental shelf. For stage (ii), we maximize the diversity of climate projections among the best-performing models. Model selection is also constrained by technical limitations, such as availability of required data from RCP2.6 and RCP8.5 projections. The selected top three CMIP5 climate models are CCSM4, MIROC-ESM-CHEM, and NorESM1-M for Antarctica and HadGEM2-ES, MIROC5, and NorESM1-M for Greenland. This model selection was designed specifically for ISMIP6 but can be adapted for other applications.

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

Geosciences ◽

10.3390/geosciences9060255 ◽

2019 ◽

Vol 9 (6) ◽

pp. 255 ◽

Cited By ~ 6

Author(s):

Thomas J. Bracegirdle ◽

Florence Colleoni ◽

Nerilie J. Abram ◽

Nancy A. N. Bertler ◽

Daniel A. Dixon ◽

...

Keyword(s):

Climate Models ◽

Climate Model ◽

Global Climate ◽

Ice Core ◽

Global Climate Models ◽

Climate Projections ◽

Past Performance ◽

Wide Range ◽

Proxy Reconstructions

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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Increased water vapour lifetime due to global warming

10.5194/acp-2019-121 ◽

2019 ◽

Cited By ~ 1

Author(s):

Øivind Hodnebrog ◽

Gunnar Myhre ◽

Bjørn H. Samset ◽

Kari Alterskjær ◽

Timothy Andrews ◽

...

Keyword(s):

Global Warming ◽

Water Vapour ◽

Solar Irradiance ◽

Climate Models ◽

Hydrological Cycle ◽

Global Climate ◽

Global Climate Models ◽

Climate Projections ◽

Surface Temperature Change ◽

Surface Warming

Abstract. The relationship between changes in integrated water vapour (IWV) and precipitation can be characterized by quantifying changes in atmospheric water vapour lifetime. Precipitation isotope ratios correlate with this lifetime, a relationship that helps understand dynamical processes and may lead to improved climate projections. We investigate how water vapour and its lifetime respond to different drivers of climate change, such as greenhouse gases and aerosols. Results from 11 global climate models have been used, based on simulations where CO2, methane, solar irradiance, black carbon (BC), and sulphate have been perturbed separately. A lifetime increase from 8 to 10 days is projected between 1986–2005 and 2081–2100, under a business-as-usual pathway. By disentangling contributions from individual climate drivers, we present a physical understanding of how global warming slows down the hydrological cycle, due to longer lifetime, but still amplifies the cycle due to stronger precipitation/evaporation fluxes. The feedback response of IWV to surface temperature change differs somewhat between drivers. Fast responses amplify these differences and lead to net changes in IWV per degree surface warming ranging from 6.4±0.9 %/K for sulphate to 9.8±2 %/K for BC. While BC is the driver with the strongest increase in IWV per degree surface warming, it is also the only driver with a reduction in precipitation per degree surface warming. Consequently, increases in BC aerosol concentrations yield the strongest slowdown of the hydrological cycle among the climate drivers studied, with a change in water vapour lifetime per degree surface warming of 1.1±0.4 days/K, compared to less than 0.5 days/K for the other climate drivers (CO2, methane, solar irradiance, sulphate).

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Prediction of the Long-Term Potential Distribution of Cryptorhynchus lapathi (L.) under Climate Change

Forests ◽

10.3390/f11010005 ◽

2019 ◽

Vol 11 (1) ◽

pp. 5 ◽

Cited By ~ 1

Author(s):

Ya Zou ◽

Linjing Zhang ◽

Xuezhen Ge ◽

Siwei Guo ◽

Xue Li ◽

...

Keyword(s):

Climate Change ◽

North America ◽

Potential Distribution ◽

Climate Models ◽

Global Climate ◽

Global Climate Models ◽

Prevention Measures ◽

Climate Data ◽

Effective Prevention ◽

Future Global Warming

The poplar and willow borer, Cryptorhynchus lapathi (L.), is a severe worldwide quarantine pest that causes great economic, social, and ecological damage in Europe, North America, and Asia. CLIMEX4.0.0 was used to study the likely impact of climate change on the potential global distribution of C. lapathi based on existing (1987–2016) and predicted (2021–2040, 2041–2080, and 2081–2100) climate data. Future climate data were simulated based on global climate models from Coupled Model Inter-comparison Project Phase 5 (CMIP5) under the RCP4.5 projection. The potential distribution of C. lapathi under historical climate conditions mainly includes North America, Africa, Europe, and Asia. Future global warming may cause a northward shift in the northern boundary of potential distribution. The total suitable area would increase by 2080–2100. Additionally, climatic suitability would change in large regions of the northern hemisphere and decrease in a small region of the southern hemisphere. The projected potential distribution will help determine the impacts of climate change and identify areas at risk of pest invasion in the future. In turn, this will help design and implement effective prevention measures for expanding pest populations, using natural enemies, microorganisms, and physical barriers in very favorable regions to impede the movement and oviposition of C. lapathi.

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Impacts of future climate change on potential yields of major crops in China

Earth System Dynamics Discussions ◽

10.5194/esdd-5-617-2014 ◽

2014 ◽

Vol 5 (1) ◽

pp. 617-647

Author(s):

Y. Yin ◽

Q. Tang ◽

X. Liu

Keyword(s):

Climate Change ◽

Climate Models ◽

Global Climate ◽

Global Climate Models ◽

Future Climate ◽

Future Climate Change ◽

Yield Reduction ◽

Climate Data ◽

Potential Yield ◽

Crop Models

Abstract. Climate change may affect crop development and yield, and consequently cast a shadow of doubt over China's food self-sufficiency efforts. In this study we used the model projections of a couple of global gridded crop models (GGCMs) to assess the effects of future climate change on the potential yields of the major crops (i.e. wheat, rice, maize and soybean) over China. The GGCMs were forced with the bias-corrected climate data from 5 global climate models (GCMs) under the Representative Concentration Pathways (RCP) 8.5 which were made available by the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP). The results show that the potential yields of rice may increase over a large portion of China. Climate change may benefit food productions over the high-altitude and cold regions where are outside current main agricultural area. However, the potential yield of maize, soybean and wheat may decrease in a large portion of the current main crop planting areas such as North China Plain. Development of new agronomic management strategy may be useful for coping with climate change in the areas with high risk of yield reduction.

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Potential for western US seasonal snowpack prediction

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1716760115 ◽

2018 ◽

Vol 115 (6) ◽

pp. 1180-1185 ◽

Cited By ~ 14

Author(s):

Sarah B. Kapnick ◽

Xiaosong Yang ◽

Gabriel A. Vecchi ◽

Thomas L. Delworth ◽

Rich Gudgel ◽

...

Keyword(s):

Sierra Nevada ◽

Climate Models ◽

Global Climate ◽

Critical Role ◽

Weather Conditions ◽

Global Climate Models ◽

Climate Indices ◽

Climate Projections ◽

Present System ◽

Western Us

Western US snowpack—snow that accumulates on the ground in the mountains—plays a critical role in regional hydroclimate and water supply, with 80% of snowmelt runoff being used for agriculture. While climate projections provide estimates of snowpack loss by the end of the century and weather forecasts provide predictions of weather conditions out to 2 weeks, less progress has been made for snow predictions at seasonal timescales (months to 2 years), crucial for regional agricultural decisions (e.g., plant choice and quantity). Seasonal predictions with climate models first took the form of El Niño predictions 3 decades ago, with hydroclimate predictions emerging more recently. While the field has been focused on single-season predictions (3 months or less), we are now poised to advance our predictions beyond this timeframe. Utilizing observations, climate indices, and a suite of global climate models, we demonstrate the feasibility of seasonal snowpack predictions and quantify the limits of predictive skill 8 months in advance. This physically based dynamic system outperforms observation-based statistical predictions made on July 1 for March snowpack everywhere except the southern Sierra Nevada, a region where prediction skill is nonexistent for every predictor presently tested. Additionally, in the absence of externally forced negative trends in snowpack, narrow maritime mountain ranges with high hydroclimate variability pose a challenge for seasonal prediction in our present system; natural snowpack variability may inherently be unpredictable at this timescale. This work highlights present prediction system successes and gives cause for optimism for developing seasonal predictions for societal needs.

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Utilization of Climate Files Predicting Future Weather in Dynamic Building Performance Simulation – A review

Journal of Physics Conference Series ◽

10.1088/1742-6596/2069/1/012070 ◽

2021 ◽

Vol 2069 (1) ◽

pp. 012070

Author(s):

C N Nielsen ◽

J Kolarik

Keyword(s):

Climate Change ◽

Climate Models ◽

Dynamical Downscaling ◽

Global Climate ◽

Global Climate Models ◽

Weather Data ◽

Building Performance ◽

Comprehensive Overview ◽

Local Climate ◽

The Impact

Abstract As the climate is changing and buildings are designed with a life expectancy of 50+ years, it is sensible to take climate change into account during the design phase. Data representing future weather are needed so that building performance simulations can predict the impact of climate change. Currently, this usually requires one year of weather data with a temporal resolution of one hour, which represents local climate conditions. However, both the temporal and spatial resolution of global climate models is generally too coarse. Two general approaches to increase the resolution of climate models - statistical and dynamical downscaling have been developed. They exist in many variants and modifications. The present paper aims to provide a comprehensive overview of future weather application as well as critical insights in the model and method selection. The results indicate a general trend to select the simplest methods, which often involves a compromise on selecting climate models.

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Climate Projections Over Different Climatic Regions of Afghanistan for Shared Socioeconomic Scenarios

10.21203/rs.3.rs-1202436/v1 ◽

2022 ◽

Author(s):

Mohammad Naser Sediqi ◽

Vempi Satriya Adi Hendrawan ◽

Daisuke Komori

Keyword(s):

Climate Models ◽

Global Climate ◽

Coupled Model ◽

Global Climate Models ◽

Maximum Temperature ◽

Climate Projections ◽

Climatic Regions ◽

Temperature And Precipitation ◽

Intercomparison Project ◽

Precipitation And Temperature

Abstract The global climate models (GCMs) of Coupled Model Intercomparison Project phase 6 (CMIP6) were used spatiotemporal projections of precipitation and temperature over Afghanistan for three shared socioeconomic pathways (SSP1-2.6, 2-4.5 and 5-8.5) and two future time horizons, early (2020-2059) and late (2060-2099). The Compromise Programming (CP) approach was employed to order the GCMs based on their skill to replicate precipitation and temperature climatology for the reference period (1975-2014). Three models, namely ACCESS-CM2, MPI-ESM1-2-LR, and FIO-ESM-2-0, showed the highest skill in simulating all three variables, and therefore, were chosen for the future projections. The ensemble mean of the GCMs showed an increase in maximum temperature by 1.5-2.5oC, 2.7-4.3 oC, and 4.5-5.3 oC and minimum temperature by 1.3-1.8 oC, 2.2-3.5 oC, and 4.6-5.2 oC for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively in the later period. Meanwhile, the changes in precipitation in the range of -15-18%, -36-47% and -40-68% for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. The temperature and precipitation were projected to increase in the highlands and decrease over the deserts, indicating dry regions would be drier and wet regions wetter.

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