The document analyzes CMIP5 model simulations of the South American Monsoon System (SAMS). SAMS is characterized by seasonal reversal of winds and increased precipitation over the Amazon basin during the summer. The study evaluates how accurately CMIP5 models simulate past and present SAMS precipitation patterns compared to observational data. Some models capture the spatial distribution of rainfall better than others, but no single model performs best in terms of variability. Future work could examine external forcings on SAMS precipitation.
Climate science part 3 - climate models and predicted climate changeLPE Learning Center
Many lines of evidence, from ice cores to marine deposits, indicate that Earth’s temperature, sea level, and distribution of plant and animal species have varied substantially throughout history. Ice cores from Antarctica suggest that over the past 400,000 years global temperature has varied as much as 10 degrees Celsius through ice ages and periods warmer than today. Before human influence, natural factors (such as the pattern of earth’s orbit and changes in ocean currents) are believed to be responsible for climate changes. For more, visit: http://www.extension.org/69150
Descriptive modeling is a type of mathematical modeling that describes major historical events and relationships between elements that created those events. Descriptive climate models typically represent significant components of the climate system like the atmosphere, oceans, land, and their interactions. One strength is they can isolate factors contributing to climate change, like how changes in precipitation and temperature affect agricultural yields. Current examples include using descriptive models to simulate 20th century climate trends and the decrease in Arctic sea ice cover since 1960.
Climate Modeling and Future Climate Change ProjectionsJesbin Baidya
Climate models are mathematical representations of the physical processes that control the climate system. The most sophisticated climate models are called General Circulation Models (GCMs) which attempt to simulate all relevant atmospheric and oceanic processes. GCMs are based on fundamental laws of physics and solve complex equations using computers. They allow scientists to project potential future climate changes from increasing greenhouse gases by assessing how the climate system may respond to restore equilibrium. While climate models have uncertainties, they provide valuable insights when evaluated against historical climate data.
Surface and soil moisture monitoring, estimations, variations, and retrievalsJenkins Macedo
The document discusses several studies related to monitoring surface and groundwater resources using remote sensing techniques.
1) One study compares soil moisture estimations from the Advanced Microwave Scanning Radiometer E (AMSR-E), ground-based measurements, and the Common Land Model (CLM). It finds that AMSR-E captures drying and wetting patterns but with lower variability than CLM or ground data.
2) Another evaluates global soil moisture from the ERS scatterometer and AMSR-E, finding general agreement except in deserts and dense vegetation due to limitations.
3) A third analyzes terrestrial water storage changes using GRACE satellite data and GLDAS land surface models,
General circulation models (GCMs) are computer models that simulate the operation of the climate system. GCMs take into account factors like greenhouse gases, landforms, ocean currents, and their interactions. GCMs are used to both identify possible causes of climate change and predict future climate. Contemporary GCMs are complex, three-dimensional models with thousands of individual cells that simulate atmospheric and oceanic processes globally. GCMs are the best tools available for determining the potential impacts of climate change and informing conservation and policy responses.
Modeling the Climate System: Is model-based science like model-based engineer...Steve Easterbrook
Keynote Talk given at the ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (Models 2015), Ottawa, September 2015.
Climate models are tools used in climate research that range in complexity from simple zero-dimensional energy balance models to complex three-dimensional general circulation models. They work by solving equations that conserve mass, momentum, energy and other quantities in grid boxes. Climate models are evaluated by comparing their results to observations. They are used for applications such as detecting and attributing causes of climate change, making projections of future climate change, and studying past climates.
Climate science part 3 - climate models and predicted climate changeLPE Learning Center
Many lines of evidence, from ice cores to marine deposits, indicate that Earth’s temperature, sea level, and distribution of plant and animal species have varied substantially throughout history. Ice cores from Antarctica suggest that over the past 400,000 years global temperature has varied as much as 10 degrees Celsius through ice ages and periods warmer than today. Before human influence, natural factors (such as the pattern of earth’s orbit and changes in ocean currents) are believed to be responsible for climate changes. For more, visit: http://www.extension.org/69150
Descriptive modeling is a type of mathematical modeling that describes major historical events and relationships between elements that created those events. Descriptive climate models typically represent significant components of the climate system like the atmosphere, oceans, land, and their interactions. One strength is they can isolate factors contributing to climate change, like how changes in precipitation and temperature affect agricultural yields. Current examples include using descriptive models to simulate 20th century climate trends and the decrease in Arctic sea ice cover since 1960.
Climate Modeling and Future Climate Change ProjectionsJesbin Baidya
Climate models are mathematical representations of the physical processes that control the climate system. The most sophisticated climate models are called General Circulation Models (GCMs) which attempt to simulate all relevant atmospheric and oceanic processes. GCMs are based on fundamental laws of physics and solve complex equations using computers. They allow scientists to project potential future climate changes from increasing greenhouse gases by assessing how the climate system may respond to restore equilibrium. While climate models have uncertainties, they provide valuable insights when evaluated against historical climate data.
Surface and soil moisture monitoring, estimations, variations, and retrievalsJenkins Macedo
The document discusses several studies related to monitoring surface and groundwater resources using remote sensing techniques.
1) One study compares soil moisture estimations from the Advanced Microwave Scanning Radiometer E (AMSR-E), ground-based measurements, and the Common Land Model (CLM). It finds that AMSR-E captures drying and wetting patterns but with lower variability than CLM or ground data.
2) Another evaluates global soil moisture from the ERS scatterometer and AMSR-E, finding general agreement except in deserts and dense vegetation due to limitations.
3) A third analyzes terrestrial water storage changes using GRACE satellite data and GLDAS land surface models,
General circulation models (GCMs) are computer models that simulate the operation of the climate system. GCMs take into account factors like greenhouse gases, landforms, ocean currents, and their interactions. GCMs are used to both identify possible causes of climate change and predict future climate. Contemporary GCMs are complex, three-dimensional models with thousands of individual cells that simulate atmospheric and oceanic processes globally. GCMs are the best tools available for determining the potential impacts of climate change and informing conservation and policy responses.
Modeling the Climate System: Is model-based science like model-based engineer...Steve Easterbrook
Keynote Talk given at the ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (Models 2015), Ottawa, September 2015.
Climate models are tools used in climate research that range in complexity from simple zero-dimensional energy balance models to complex three-dimensional general circulation models. They work by solving equations that conserve mass, momentum, energy and other quantities in grid boxes. Climate models are evaluated by comparing their results to observations. They are used for applications such as detecting and attributing causes of climate change, making projections of future climate change, and studying past climates.
Surface Soil Moisture and Groundwater Assessment and Monitoring using Remote ...Jenkins Macedo
This preview is part of the requirement for a comprehensive analysis of remotely sensed surface soil moisture and groundwater assessment and monitoring for global environmental and climate change presented by Christina Geller, candidate for the degree of MSc in Geographic Information Science for Development, and Environment and Jenkins Macedo, candidate for the MS in Environmental Science and Policy at the Department of International Development, Community, and Environmental at Clark University.
This is the 7th lesson the course - Climate Change & Global Environment taught at the Faculty of Social Sciences and Humanities of the Rajarata University of Sri Lanka
This document outlines key concepts related to climate and climate modelling. It discusses global climate models (GCMs) which are 3D models of the atmosphere and oceans used to simulate the climate system. It also discusses regional climate models (RCMs) which provide higher resolution outputs than GCMs to better represent regional features. The document then summarizes projected climate changes from the Intergovernmental Panel on Climate Change including increased global temperatures, precipitation changes, and sea level rise under different emissions scenarios through 2100.
This document summarizes a presentation on climate data and projections focusing on limiting global warming to less than 2 degrees Celsius. It discusses the work of GERICS (the Climate Service Center Germany) in developing solutions for regional climate modeling, impacts analysis, and climate adaptation toolkits. Key points covered include:
- GERICS' interdisciplinary approach to regional climate modeling, impacts assessment, and stakeholder engagement.
- The development of adaptation toolkits for cities, companies, and other sectors to facilitate climate risk assessment and planning.
- An overview of the presentation, covering topics like climate modeling techniques, accessing climate projections data, and visualizing and analyzing climate information.
Downscaling global climate model outputs to fine scales over sri lanka for as...Pixel Clear (Pvt) Ltd
This proposal seeks funding to downscale global climate model outputs to finer scales over Sri Lanka in order to better assess drought impacts. The objectives are to 1) downscale historical data using statistical and dynamic methods, 2) compute drought indices from the downscaled data and assess their ability to capture past droughts, and 3) downscale future projections to characterize uncertainty in future drought tendencies. Downscaled data will be evaluated against gridded observed drought indices. The goal is to improve understanding of how well models capture Sri Lankan climate variability and drought, and assess near-term climate change impacts on drought with associated uncertainty.
Climate Modelling, Predictions and Projectionsipcc-media
This document discusses climate modeling, predictions, and projections. It summarizes that global surface temperature change is likely to exceed 1.5°C by the end of the century for all scenarios. It also notes that ocean acidification is a clear signal of human-caused climate change and that global sea levels will continue rising through 2100 even with reductions in greenhouse gas emissions. Initialized climate simulations can reproduce temperature trends and internal variability to provide near-term climate predictions.
1) CGCMs are coupled general circulation models that combine atmospheric and oceanic GCMs to allow the lower boundary conditions of the atmosphere to be determined interactively by ocean processes.
2) CGCM3.1 is a third generation CGCM developed by CCCMA that runs at T47 and T63 resolutions with ocean grids of roughly 1.4x0.9 degrees and 1.85 degrees respectively.
3) A study assessed CGCM3.1's wind fields in the Persian Gulf, finding it generally underestimated wind speeds compared to ECMWF reanalysis data.
To aid in understanding many complex interactions, scientists often build mathematical models that represent simple climate systems. This module highlights the fundamentals of climate models.
Analyzing climate change risks and vulnerabilitiesNAP Events
The document discusses analyzing climate change risks and constructing climate scenarios for national adaptation plans (NAPs) in the Pacific region. It defines climate scenarios as representations of future climate based on climate modeling used to assess potential climate change impacts. The document outlines different types of climate scenarios, including those based on incremental changes, analogues, and climate models. It describes generating regional climate projections through dynamically and statistically downscaling global climate model data to finer scales. Examples are provided of accessing existing climate data and projections, and applying climate scenarios to assess potential impacts like changes in global banana production suitability under climate change.
Slides from a presentation about modeling past and future climate as part of the "School of Ice" workshop for educators at Oregon State University on Aug. 2, 2021.
This document summarizes a study analyzing extreme heat in Maricopa County, Arizona between 2005-2015. The study aims to 1) analyze days with extreme heat anomalies, 2) establish how users of heat relief resources correlate with socioeconomic vulnerability, and 3) determine where, when and how relief efforts should intervene. Satellite imagery and census data are used to classify land use, measure surface temperatures, and identify vulnerable populations. Results found shifts in average surface temperatures between early, mid, and late summer seasons. Future work will further employ land use classification and weather data to better understand heat variations and identify optimal solar panel locations.
The document discusses methods for estimating evapotranspiration (ET) using the radiation method. It describes ET as the sum of evaporation and plant transpiration from the earth's surface to the atmosphere. The radiation method uses climatological data like solar radiation, air temperature, and humidity to estimate ET. It also discusses factors that affect ET rates and provides equations to compute reference ET values from meteorological measurements for agricultural planning and irrigation scheduling.
This document describes a study that developed a method for mapping annual land cover and land use (LULC) in the Dry Chaco ecoregion of South America using MODIS satellite imagery. Reference data was collected by visually interpreting high-resolution QuickBird imagery in Google Earth at random sample points. LULC was classified into 8 classes using predictor variables derived from MODIS time series data and a Random Forests classifier. Annual LULC maps from 2001 to 2007 were produced at 250m resolution and assessed for accuracy. The maps showed rapid deforestation related to expansion of soybean and pasture agriculture.
Presentation on Aerosols, cloud properties Esayas Meresa
This slide was prepared for the course Applications of GIS and RS for water resources in Mekelle University, Institute of Geo-information and earth observation Science(I-GEOS) by Mr. Esayas Meresa.
Linking the Quasi-Biennial Oscillation and Projected Arctic Sea-Ice Loss to S...Zachary Labe
This document summarizes a study examining how the Quasi-Biennial Oscillation (QBO) can modulate the atmospheric response to projected Arctic sea ice loss. The study uses the Whole Atmosphere Community Climate Model to simulate different phases of the QBO and responses to historical and future sea ice conditions. It finds that a weaker early winter polar vortex occurs during the QBO's easterly phase, leading to constructive wave interference. A North Atlantic Oscillation-like response is seen in the QBO's westerly phase, while Siberian cold extremes occur in the easterly phase. The results suggest the QBO can influence both stratospheric and tropospheric/surface responses through the Holton-
The document discusses climate change in the Arctic. It notes that the Arctic is warming faster than the rest of the globe, with sea ice extent and thickness declining significantly. Climate models project that Arctic warming and sea ice loss will continue through the 21st century. Improving observations and models can help reduce uncertainties about future climate impacts in the Arctic and how changes may influence remote weather patterns. Action is needed to reduce emissions and limit global temperature rise in order to prevent the worst effects of climate change in the Arctic.
Remote sensing uses sensors on aircrafts and satellites to obtain information about objects and areas from a distance. It has various applications such as observing ocean currents, preventing wetland degradation, quantifying earthquake damage through change detection between pre- and post-earthquake images, and comparing past and present climatic factors by mapping variables over time from NASA satellites. Remote sensing is also used to monitor vehicle emissions and fuel economy from space with minimal interference and classify landscape images into categories.
Southern Hemisphere atmospheric circulation: impacts on Antarctic climate and...Andrew Russell
Presentation given at the PAGES symposium in Chile in October 2010. (NB I gave this talk before O'Donnell et al. was published so I'd probably do it differently now.)
This document summarizes an integrated hydrological study of the entire Nile River basin led by NASA and its partners. The study uses NASA satellite observations and modeling tools to provide estimates of water resources and fluxes across the large basin, in order to support decision making. Partners in various Nile basin countries will help validate and apply the modeling results for drought monitoring, water resource planning, and early warning systems. The goal is to improve water management amid development and climate change challenges across the many countries sharing the Nile River system.
The document summarizes a regional training workshop held in Malawi on constructing climate change scenarios for use in National Adaptation Plans in Anglophone Africa. It provides background on observed and projected changes in climate for the region based on climate models and the IPCC assessment reports, including increasing temperatures, more extreme rainfall and drought events. Methods covered include constructing incremental, analogue and model-based climate scenarios from global and regional climate models as well as statistically downscaling global projections. Examples are given of applying climate scenarios to assess potential impacts on Malawi's growing season length and variability.
The document discusses analyzing climate change risks and constructing climate scenarios for developing national adaptation plans. It describes defining climate scenarios using climate projections and models at global and regional scales. Different types of climate scenarios are outlined, including those based on incremental changes, analogues, and climate models. Methods for generating and accessing climate scenarios from global datasets like CORDEX are also summarized.
Surface Soil Moisture and Groundwater Assessment and Monitoring using Remote ...Jenkins Macedo
This preview is part of the requirement for a comprehensive analysis of remotely sensed surface soil moisture and groundwater assessment and monitoring for global environmental and climate change presented by Christina Geller, candidate for the degree of MSc in Geographic Information Science for Development, and Environment and Jenkins Macedo, candidate for the MS in Environmental Science and Policy at the Department of International Development, Community, and Environmental at Clark University.
This is the 7th lesson the course - Climate Change & Global Environment taught at the Faculty of Social Sciences and Humanities of the Rajarata University of Sri Lanka
This document outlines key concepts related to climate and climate modelling. It discusses global climate models (GCMs) which are 3D models of the atmosphere and oceans used to simulate the climate system. It also discusses regional climate models (RCMs) which provide higher resolution outputs than GCMs to better represent regional features. The document then summarizes projected climate changes from the Intergovernmental Panel on Climate Change including increased global temperatures, precipitation changes, and sea level rise under different emissions scenarios through 2100.
This document summarizes a presentation on climate data and projections focusing on limiting global warming to less than 2 degrees Celsius. It discusses the work of GERICS (the Climate Service Center Germany) in developing solutions for regional climate modeling, impacts analysis, and climate adaptation toolkits. Key points covered include:
- GERICS' interdisciplinary approach to regional climate modeling, impacts assessment, and stakeholder engagement.
- The development of adaptation toolkits for cities, companies, and other sectors to facilitate climate risk assessment and planning.
- An overview of the presentation, covering topics like climate modeling techniques, accessing climate projections data, and visualizing and analyzing climate information.
Downscaling global climate model outputs to fine scales over sri lanka for as...Pixel Clear (Pvt) Ltd
This proposal seeks funding to downscale global climate model outputs to finer scales over Sri Lanka in order to better assess drought impacts. The objectives are to 1) downscale historical data using statistical and dynamic methods, 2) compute drought indices from the downscaled data and assess their ability to capture past droughts, and 3) downscale future projections to characterize uncertainty in future drought tendencies. Downscaled data will be evaluated against gridded observed drought indices. The goal is to improve understanding of how well models capture Sri Lankan climate variability and drought, and assess near-term climate change impacts on drought with associated uncertainty.
Climate Modelling, Predictions and Projectionsipcc-media
This document discusses climate modeling, predictions, and projections. It summarizes that global surface temperature change is likely to exceed 1.5°C by the end of the century for all scenarios. It also notes that ocean acidification is a clear signal of human-caused climate change and that global sea levels will continue rising through 2100 even with reductions in greenhouse gas emissions. Initialized climate simulations can reproduce temperature trends and internal variability to provide near-term climate predictions.
1) CGCMs are coupled general circulation models that combine atmospheric and oceanic GCMs to allow the lower boundary conditions of the atmosphere to be determined interactively by ocean processes.
2) CGCM3.1 is a third generation CGCM developed by CCCMA that runs at T47 and T63 resolutions with ocean grids of roughly 1.4x0.9 degrees and 1.85 degrees respectively.
3) A study assessed CGCM3.1's wind fields in the Persian Gulf, finding it generally underestimated wind speeds compared to ECMWF reanalysis data.
To aid in understanding many complex interactions, scientists often build mathematical models that represent simple climate systems. This module highlights the fundamentals of climate models.
Analyzing climate change risks and vulnerabilitiesNAP Events
The document discusses analyzing climate change risks and constructing climate scenarios for national adaptation plans (NAPs) in the Pacific region. It defines climate scenarios as representations of future climate based on climate modeling used to assess potential climate change impacts. The document outlines different types of climate scenarios, including those based on incremental changes, analogues, and climate models. It describes generating regional climate projections through dynamically and statistically downscaling global climate model data to finer scales. Examples are provided of accessing existing climate data and projections, and applying climate scenarios to assess potential impacts like changes in global banana production suitability under climate change.
Slides from a presentation about modeling past and future climate as part of the "School of Ice" workshop for educators at Oregon State University on Aug. 2, 2021.
This document summarizes a study analyzing extreme heat in Maricopa County, Arizona between 2005-2015. The study aims to 1) analyze days with extreme heat anomalies, 2) establish how users of heat relief resources correlate with socioeconomic vulnerability, and 3) determine where, when and how relief efforts should intervene. Satellite imagery and census data are used to classify land use, measure surface temperatures, and identify vulnerable populations. Results found shifts in average surface temperatures between early, mid, and late summer seasons. Future work will further employ land use classification and weather data to better understand heat variations and identify optimal solar panel locations.
The document discusses methods for estimating evapotranspiration (ET) using the radiation method. It describes ET as the sum of evaporation and plant transpiration from the earth's surface to the atmosphere. The radiation method uses climatological data like solar radiation, air temperature, and humidity to estimate ET. It also discusses factors that affect ET rates and provides equations to compute reference ET values from meteorological measurements for agricultural planning and irrigation scheduling.
This document describes a study that developed a method for mapping annual land cover and land use (LULC) in the Dry Chaco ecoregion of South America using MODIS satellite imagery. Reference data was collected by visually interpreting high-resolution QuickBird imagery in Google Earth at random sample points. LULC was classified into 8 classes using predictor variables derived from MODIS time series data and a Random Forests classifier. Annual LULC maps from 2001 to 2007 were produced at 250m resolution and assessed for accuracy. The maps showed rapid deforestation related to expansion of soybean and pasture agriculture.
Presentation on Aerosols, cloud properties Esayas Meresa
This slide was prepared for the course Applications of GIS and RS for water resources in Mekelle University, Institute of Geo-information and earth observation Science(I-GEOS) by Mr. Esayas Meresa.
Linking the Quasi-Biennial Oscillation and Projected Arctic Sea-Ice Loss to S...Zachary Labe
This document summarizes a study examining how the Quasi-Biennial Oscillation (QBO) can modulate the atmospheric response to projected Arctic sea ice loss. The study uses the Whole Atmosphere Community Climate Model to simulate different phases of the QBO and responses to historical and future sea ice conditions. It finds that a weaker early winter polar vortex occurs during the QBO's easterly phase, leading to constructive wave interference. A North Atlantic Oscillation-like response is seen in the QBO's westerly phase, while Siberian cold extremes occur in the easterly phase. The results suggest the QBO can influence both stratospheric and tropospheric/surface responses through the Holton-
The document discusses climate change in the Arctic. It notes that the Arctic is warming faster than the rest of the globe, with sea ice extent and thickness declining significantly. Climate models project that Arctic warming and sea ice loss will continue through the 21st century. Improving observations and models can help reduce uncertainties about future climate impacts in the Arctic and how changes may influence remote weather patterns. Action is needed to reduce emissions and limit global temperature rise in order to prevent the worst effects of climate change in the Arctic.
Remote sensing uses sensors on aircrafts and satellites to obtain information about objects and areas from a distance. It has various applications such as observing ocean currents, preventing wetland degradation, quantifying earthquake damage through change detection between pre- and post-earthquake images, and comparing past and present climatic factors by mapping variables over time from NASA satellites. Remote sensing is also used to monitor vehicle emissions and fuel economy from space with minimal interference and classify landscape images into categories.
Southern Hemisphere atmospheric circulation: impacts on Antarctic climate and...Andrew Russell
Presentation given at the PAGES symposium in Chile in October 2010. (NB I gave this talk before O'Donnell et al. was published so I'd probably do it differently now.)
This document summarizes an integrated hydrological study of the entire Nile River basin led by NASA and its partners. The study uses NASA satellite observations and modeling tools to provide estimates of water resources and fluxes across the large basin, in order to support decision making. Partners in various Nile basin countries will help validate and apply the modeling results for drought monitoring, water resource planning, and early warning systems. The goal is to improve water management amid development and climate change challenges across the many countries sharing the Nile River system.
The document summarizes a regional training workshop held in Malawi on constructing climate change scenarios for use in National Adaptation Plans in Anglophone Africa. It provides background on observed and projected changes in climate for the region based on climate models and the IPCC assessment reports, including increasing temperatures, more extreme rainfall and drought events. Methods covered include constructing incremental, analogue and model-based climate scenarios from global and regional climate models as well as statistically downscaling global projections. Examples are given of applying climate scenarios to assess potential impacts on Malawi's growing season length and variability.
The document discusses analyzing climate change risks and constructing climate scenarios for developing national adaptation plans. It describes defining climate scenarios using climate projections and models at global and regional scales. Different types of climate scenarios are outlined, including those based on incremental changes, analogues, and climate models. Methods for generating and accessing climate scenarios from global datasets like CORDEX are also summarized.
This document describes The Climate Data Factory, a service that aims to make climate projection data easier to access and use for non-climate scientists. It notes that preparing and working with raw climate model data is currently difficult and time-consuming for most users due to issues like different grids, bias, and data volume. The Climate Data Factory addresses these problems by providing re-gridded, bias-corrected, quality-controlled climate model projections that can be easily searched and accessed through their website. This is intended to help various audiences like impact researchers, adaptation practitioners, and consulting engineers make more effective use of climate model data.
This document discusses using an Earth System Model (ESM) based on the NCEP Climate Forecast System (CFS) to project future changes in the South Asian monsoon under changing climate conditions. It notes challenges in modeling the monsoon including uncertainties in present-day simulations. It outlines the ESM development strategy at the Indian Institute of Tropical Meteorology including incorporating aerosol, biogeochemistry and ecosystem modules into CFS. Validation of CFS shows reasonable representation of climatological rainfall and variability. Analyses of CFS droughts suggest atmosphere-ocean coupling and monsoon-midlatitude interactions can influence droughts.
The document describes the challenges of working with climate model data, including large volumes of data, difficulties finding and accessing data from different models and grids, and the need for bias correction and quality control. It then introduces the Climate Data Factory as an innovative service that addresses these issues by re-mapping, bias adjusting, quality controlling and simplifying access to raw CMIP5 and CORDEX climate model data to make it easier to use for impact researchers, adaptation practitioners, and consulting engineers.
Climate models are mathematical representations of physical processes that determine climate. They are used to understand climate processes and project future climate scenarios. Simplifications are needed due to complex interactions and limited computational capabilities. Models have improved over time with increased resolution and process representation. Observational evidence shows unequivocal warming globally with some regional precipitation variability. Projections show continued warming and changes in precipitation patterns for South Asia over the 21st century, but models have uncertainties. Continued improvements aim to better capture regional climate impacts.
This document summarizes challenges in accessing, preparing, and using climate model data for research. It notes that a large volume of climate model data is being produced but is difficult to access and use, particularly for non-climate scientists, as the data is on different grids, may need bias correction, and requires significant time and effort to prepare. Several papers are cited that found most researchers spend over 80% of their time preparing climate data rather than using it. The document discusses ongoing work to address these issues through initiatives like bias correction and the climate data factory project to help process and provide access to model outputs.
This document summarizes the key findings and challenges from a U.S. climate change science workshop. It identifies major questions around climate feedbacks and predictability. Key challenges include uncertainties around climate sensitivity and natural variability. The document calls for improved climate models, observations, and communication of information to decision-makers.
The document discusses equations of motion used in weather forecasting and climate change studies. It begins with an introduction to geophysical fluid dynamics and the distinguishing effects of rotation and stratification. It then outlines the basic equations of motion, including conservation of momentum, mass, energy, and state. It describes how these equations are solved on grids using numerical models. It discusses the challenges of modeling processes at different spatial scales from synoptic to urban. It also addresses challenges in tropical weather prediction and how dynamical prediction of weather over South Asia has improved.
This document provides a summary of Brian Medeiros' career and qualifications. It includes his educational background, earning a PhD in Atmospheric & Oceanic Sciences from UCLA in 2007. It lists his professional experience as a Project Scientist at NCAR since 2009. It also provides details of his research focus on cloud-climate interactions in general circulation models, awards, teaching experience, research grants, publications, and professional service and involvement.
Climate change is projected to impact drastically in southern African during the 21st century
under low mitigation futures (Niang et al., 2014). African temperatures are projected to rise
rapidly, in the subtropics at least at 1.5 times the global rate of temperature increase (James
and Washington, 2013; Engelbrecht et al., 2015). Moreover, the southern African region is
projected to become generally drier under enhanced anthropogenic forcing (Christensen et
al., 2007; Engelbrecht et al., 2009; James and Washington, 2013; Niang et al., 2014). These
changes in temperature and rainfall patterns will plausibly have a range of impacts in South
Africa, including impacts on energy demand (in terms of achieving human comfort within
buildings and factories), agriculture (e.g. reductions of yield in the maize crop under higher
temperatures and reduced soil moisture), livestock production (e.g. higher cattle mortality as
a result of oppressive temperatures) and water security (through reduced rainfall and
enhanced evapotranspiration) (Engelbrecht et al., 2015).
This document summarizes a study assessing the risks of climate change on urban development and drainage. It outlines the objectives to evaluate the impact of climate change on urban rainfall extremes and revise drainage design criteria. The methodology uses climate models and downscaling to estimate impacts and urban rainfall-runoff simulation to obtain runoff data. A literature review covers topics like drainage infrastructure design, sewer systems, urban resilience, and impacts of climate change. The conclusions state that evaluating climate change impacts increases drainage reliability and development needs to consider adaptation, mitigation and resilience.
Scott McIntosh, Director, High Altitude Observatory, National Center for Atmospheric Research, Boulder, Colorado
June 2016 - UCAR Congressional Briefing on Predicting Space Weather
Video of this presentation will be available soon.
This is the paper for our final project in our Numerical Weather Prediction class. For this project, we analyzed model output from a Nested Regional Climate Model (NRCM), which is an adaptation of the Advanced Research WRF (ARW). The model output variables analyzed were outgoing long wave radiation (OLR) and precipitation (convective plus non-convective). The goal of this research project was to determine why errors were occurring in the model, and what could be done to correct them. In this paper, we provide some insight into why these errors occurred, particularly errors within the model which equaled or surpassed the overall mean climate error.
Goswami Climate Change And Indian Monsoon Cse Workshopequitywatch
This document discusses key issues related to climate change and the Indian monsoon. It summarizes that while global temperatures are rising, the mean Indian monsoon rainfall has not shown an increasing trend over the past 100 years. Some extreme rainfall events have increased in intensity. Climate models have high uncertainty in projecting changes to the monsoon under climate change scenarios. Developing high resolution global and regional climate models is crucial for reducing uncertainty but significant challenges remain.
The document evaluates the performance of the TRMM Multi-satellite Precipitation Analysis (TMPA) product in estimating daily precipitation in the Central Andes region, compared to gauge measurements. It finds large biases in daily precipitation amounts from TMPA for the regions of Cuzco, Peru and La Paz, Bolivia, though strong precipitation events are generally detected. Correlation with gauge data increases significantly when aggregating TMPA estimates to longer time periods like weekly or monthly sums. Spatial aggregation has little effect on performance. The document proposes blending TMPA with daily gauge data to improve daily estimates.
This document discusses Dr. Cody Knutson's research on drought vulnerability and planning in the North Central region of the United States. It summarizes several of his projects analyzing drought impacts through surveys and interviews of farmers, ranchers, and other stakeholders. It also discusses the development of decision support tools incorporating climate and crop models, as well as assessments of decadal climate variability and predictability in the Missouri River Basin. The document provides an overview of Dr. Knutson's work developing and applying methodologies for drought vulnerability assessment and planning across multiple sectors.
This document summarizes a master's thesis that evaluates the effects of precipitation extremes on watershed hydrology under current and projected future climate conditions using the Soil and Water Assessment Tool (SWAT) model. The research focuses on the Cobb Creek Watershed in Georgia. Results show that high intensity precipitation events could increase watershed discharge by nearly 50% by 2060-2064 according to climate projections. Peak flows may rise by almost 30% and shifts in seasonal rainfall patterns are also possible. The study highlights the need for sustainable water management and planning that considers potential climate change impacts.
Planning For Climate Change In The Technical Analysis 6 9 09Michael DePue
The document discusses how climate change trends should be incorporated into floodplain mapping and flood control project planning. It summarizes reports on topics like increased precipitation and sea level rise. It recommends considering a range of climate change scenarios in technical analyses, like higher sea levels and more intense storms. Adaptation strategies may include revised flood maps, upgraded infrastructure, and modified planning guidelines.
Planning For Climate Change In The Technical Analysis 6 9 09
CMIP5 Model Analysis
1. Analysis of CMIP5 on the South
American Monsoon System
(SAMS)
James Duncan
2. South American Monsoon System (SAMS)
• A monsoon can be described as a seasonal reversal in the large-scale
surface winds driven by heating between the land and ocean.
• Large seasonal changes result in an increase of precipitation over the
Amazon basin and the establishment of an upper-level anticyclone
known as the Bolivian High, and he Chaco low in northwest Argentina
and Paraguay.
3. CMIP5 Model Analysis
The fifth phase of the Coupled Model Intercomparison Project
(CMIP5) was designed to evaluate how accurate the models
are in simulating past, present, and future projections of climate.
Verification
Gridded Precipitation Data from the Global Precipitation
Climatology Project (GPCP) (1979-2005)
Constructed by combining records from rain gauge stations
merged with observations (satellite geostationary, low-orbit
infrared, passive microwave, and sounding observations).
11. IMF 1 IMF 1
IMF 2 IMF 2
IMF 3 IMF3
IMF 4
IMF 4
IMF1 IMF 1
IMF 2 IMF 2
IMF 3 IMF 3
IMF 4 IMF 4
12. Future Work/Conclusion
• Cannot quantitively say which models performed better than
others.
• While one model may better represent the spatial distribution
of precipitation in association with the monsoon, it may do
horrible in terms of variability.
• Look into outside forcings to SAMS precipitation.
13. Questions?
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Editor's Notes
Things that play a role: -ENSO (largest known forcing of interannual variability) -strength, structure, and variability of SALLJ Large-scale thermally direct circulationw ith a continental rising branch and an oceanic sinking branch, land-atmosphere interactions associated with elevated terrain and land surface conditions, surface low pressure and an upper level anticyclone, intense low-level inflow of moisture to the continent, and associated seasonal changes in precipitation Note the seasonal shift in convection across the South America Continent Prominent feature of the SAMS is the presence of a northwest-southeast oriented band of clouds and precipitation that originate in the Amazon and run toward the subtropical Atlantic
Drawbacks associated with GPCP (5 by 5 degree resolution) -satellite (1979-2005) -rain gauge station density is another, one gridded area could be dependent upon a few things, recording techniques, possible elimination of extreme values
Did this time span so we can fully see how the models pick up on the onset and demise of SAMS -want to know spatial pattern of the onset/demise, rather than just peak months. Much of interannual variability is due to discrepencies in the date of the onset/demise, thus if you don’t get this right, cant expect to get interannual or mean conditions correct Prominent feature of the SAMS is the presence of a northwest-southeast oriented band of clouds and precipitation that originate in the Amazon and run toward the subtropical Atlantic HadGEM does the best job of replicating the orientation of the band of convection while also matching appropriate intensity. Prevents build up of the max except along the Peruvian coast which all models have the same bias to do so. Ability to resits a preffered max MIROC5 and CCMS4 overestimate precipitation at preferred locations CCSM4 pocket in northeastern Brazil MIROC5 illustrates a max along central to southeastern Brazil With the demise, HadGEM2 illustrates too strong of a demise, not observations see a slow track retreat of this band, not a complete shutoff of max convection. With this model in the months of March and April, a band of convection cannot be noted across the area. The CCSM4 does not really show any change to it’s pocket of precip in the northeast portion of Brazil, shows strong persistence in this max, possible interaction between ITCZ and land, near the mouth of the Amazon MIROC5 actually provides a fairly well nice representation of the slow retreat of the system.
As from before, the HadGEM appears to do the best job of spatially representing precipitation across the greater amazon basin. All models appear to want to pick up on some form of a double max located near columbia, over amplication of this secondary eastward max. The CCSM4 does not pick up on the southward extend of the monsoon season and the MIROC5 has too southward of an extension (think though, if ccsm4 does not extend much southward, should we expect large varaiblity values that low) Had’s propensity to moderate things will hurt it later
A model is not based upon its ability to repoduce climatology, rather its measured based on its ability to reporoduce the variability within atmospheric states Absolute Average deviation is a statistical measure of disperision, finding the average anomalous precipitation -it has been noted areas of high AAD, have high interannaul varaib -does not tell us whether it is over or underestimating, just the ability of the model to measure varaiiblity -only problem with this is the selection of central tendency (Urugauy precipiation is associated with El Nino, also norhter SA and southern Brazil, northeast Argentina) -from this can note possible abilities of the model to replicate ENSO forcings -can note from this that in some areas, the models are heavily overestimating varaibility from the mean, or from year to year -once again the HadGEM appears to do the best job in keeping variability to a minimum over land, not the case of the oceans though where it produces double all sorts of ITCZ -both the MIROC and CCSM4 produce regions of large varaibility in association iwht their little pockets of maximum precip -not much variability int hat secondary maximum near Columbia -no models picked up on the zonal band of variability across the Amazon/Brazil
Another advantage to this topic is that we know that all the models tried to make a better visuallization of the ENSO, but was this sufficient, compare models to the next and see
Greatest discrepency is located near the Amazon Delta -show a rise in precipitation associated with the ‘summer month when observations is whoign a steady drop. Off on initial conditions and -to much dependence upon the summer/seasonal cycle, problem with dynamics in location to the equator, resolution at the equator NWSA, too much of reaction to the NH summer seaso -look at the other plots, the HadGEM appears to stay with GPCP most consistently of the bunch -consistently through the plots, it appears as though MIROC5 and CCSM4 are very sensitive to the hemispheric winter/summer whether it be dry or wet patterns.
Backing away from the equator, the CCSM4 and MIROC5 seem to have a better handle on the seasonal months Southern portions of the continetn, seemed ot do a much better job of replication, possibly due to distance away from the equator
40 to 70 20 to 0 which I took to be representative of the monsoon season Note: Wave Pattern
Data can show good correlations, however corresponding IMFS may show little in terms of synchronization. The reason isthat the underlying physical processes that dictate the large-scale interactions between atmosphere and ocean differ on various timescales. Extract physically meaningfual signals from the data While the HadGEM appeared to get the best spatial correlation, you can see here that the oscillations/forcings are off -both the HAD and MIROC show an inability to represent the annual cycles within the first IMF early on (is this solely because of our time scale?) Higher IMFs see lower frequency events -and when it comes to these lower frequency modes, the models are able to produce anytype of a cycle if not evne of the wrong sign. The CCSM4 is the only model that is able to reproduce these oscillations consistenly at both high and low end frequencies
Cannot qualittatively say that one model is better than the other, rather that certain are better in terms of one thing or the other All models have their strenghts/weaknesses