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Innovative Web Based Applications of High Resolution Climate Modeling

and Projections Techniques

CCRC, Columbia, SCOctober 31, 2018

Jenny Dissen, Engagement and PartnershipsNOAA Cooperative Institute for Climate and Satellites - NC

North Carolina State University

Contributors/Acknowledgements:Dr. Katharine Hayhoe (Texas Tech University); Dr. David Easterling (NOAA NCEI); Dr. Kenneth Kunkel

(NOAA CICS-NC); Andrew Ballinger, Ph.D., Research Scholar; Dr. Farhan Akhtar (US Department of State)

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Agenda

Indo-U.S. Partnership for Climate Resilience

Engagement Activities

Climate Analysis Tool

Looking Ahead…

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U.S.-India Partnership for Climate Resilience (US PCR)

• PCR initiated in September 2014 between President Obama and Prime Minister Modi

• Goals:• Advance our bilateral climate change relationship• Enable technical expertise and information exchange• Support strengthening of adaptation and resilience

planning and capabilities in regions of India

India Partners Involved:

• Officials in the Ministry of Earth Sciences

(MoES); Ministry of Environment, Forest &

Climate Change (MoEF&CC);

• Pune-based Indian Institute for Tropical

Meteorology (IITM);

• NGOs: EPTRI and TERI

• Private sector: Value Labs

U.S. Partners:

• Interagency Agreement (IAA) between

Department of State and NOAA

• NOAA National Centers for Environmental

Information

• NOAA Cooperative Institute for Climate and

Satellites – NC / NC State University

• Several university partners (Texas Tech

University)

• World Resources Institute

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India’s Policy – National and State Action Plans

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Workshops on Development and Applications of Climate Projections

• Downscaling Techniques and Applications• March, 2017 | IITM, Pune• Technical discussions, exercise, select applications

• High Resolution Climate Modeling Overview and Exercise• February 9, 2018 | The Energy and Resources Institute (TERI)

• High Resolution Climate Modeling Overview and Exercise• February 12 - 13, 2018 | EPTRI• Targeted for State Action Planners

• Climate and Health Workshop• October 23-24, 2018 | New Delhi• Vector-Borne Disease, Malaria, Predictive Capabilities

• Proposed: World Sustainable Development Summit (To be finalized)

• February 11 - 13, 2019 | New Delhi and Northern India• Technical workshop on climate modeling and projections

• Workshop focused on the India Himalayan Range

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Technical Sessions

Theories and methods of climate modeling, projections

and downscaling

Explorations of specific downscaled data sets—including

the World Climate Research Program’s CORDEX data

on South Asia

GFDL-NCPP “Perfect Model” Model Approach to

Comparing Downscaling Methods

Introduction to Asynchronous Regional Regression

Model (ARRM) and NASA Earth Exchange Global Daily

Downscaled Projections (NEX-GDDP) Outputs

Uses and Applications for Decision Making

• Case Study in Uttarakhand

• India State Action Plans and Vulnerability Risk

Assessments ... cases from several Indian states

• Sectoral examples: agriculture, infrastructure and

urban planning, water resources management

Discussion on downscaled climate information and its

application potentials in various sectors

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Workshop Experience

• Overall, a tremendous success – but:

• Participants challenged with:

• R (a programming language and IDE)

• Panoply (a simple graphical tool for

viewing climate data maps)

• Exercise used open-source resources

• Installation challenges

• Had to learn R code and various

libraries

• Code and Data (unwieldy)

• Download ~21 GB of data time

consuming

• Dr. Hayhoe’s code and data are on a

few thumb drives being passed

around…

• Biggest stumbling block: finding the code

and the data in R

• Second Biggest Stumbling Block:

misreading (or not reading) the instructions.

• Oops. I forgot the parentheses!

• Let’s try that again

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As a Result…

• EPTRI partnered with Value Labs to address

user experience and usability concerns

• Creating a web-based platform to visualize

downscaled data Climate Analysis Tool

(work in progress)

• Key Features:

• User not required to code in R

• Graphs and charts populated in the tool

• Improved user interaction with simple

drop down selection options

• Stakeholder no longer burdened with

issues with data and programming and

• Participants spending more time

discussing outcomes, results and

implications of changing climatic

conditions

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1. Analyze climate model

output at a location:• Observe and compare

trends by station data

• You will have the ability to

select various parameters

and indicators

• Download charts to

embed in presentations

• Download the data files

2. … and over the entire

region (or sub-region):• Observe spatial patterns

• Explore different future

climate periods

Tool Capabilities

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How often will temperature exceed a threshold?

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Features to Analyze Climate Questions

• Analyze different variables• Maximum daytime temperature• Minimum nighttime temperature• Average daily temperature• Precipitation• Degree-days

• Assess different indicators • Annual or seasonal average • Annual or season maximum or

minimum • The number of days above or below

a given threshold• Seasonal variations

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Annual Average Daily Maximum Temperature

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Average Total Rainfall from July to September

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Policy Innovation Support in Climate Assessments

• Translate climate projections into

relevant impacts information using

state-of-the-art, well-documented

climate models and well-evaluated

climate downscaling approaches

• Interact extensively with the

stakeholders

• Communicate findings to the

intended audience in a relevant,

accessible manner

The collaboration to build Climate Analysis Tool supports in climate assessments:

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Looking Ahead…

Further review, validate and develop additional features in the tool

Work with decision-makers and State Action Planners to incorporate their state information for use and analysis

Build engagement capacity discussions to assist decision-makers with understanding and using outputs

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More Information

Indo-U.S. Team

• David Easterling2

• Kenneth Kunkel1

• Jenny Dissen1

• Andrew Ballinger1

• Farhan Akhtar4

• Katharine Hayhoe5

• Anne Stoner5

• Bridget Thrasher6

Author Affiliation:1. Cooperative Institute for Climate and Satellites -

North Carolina / NC State University, Asheville, North Carolina

2. NOAA National Centers for Environmental Information, Center for Weather and Climate, Asheville, NC

3. U.S. Department of State, Washington D.C.4. Texas Tech University5. Stanford University

• Kalyan Chakraborthy

• Dr. Sesha Srinivas

• Dr. Ramesh

• Shaily Maloo• Dr. Ashwini Kulkarni (IITM Pune)

• Praveen Chakravarthula

• Sai Krishna Nooka

• Kiran Jangeti• Satyla Styavarapu

US Team India Team Value Labs

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Back Up Slides

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Overview of the station-based dataset:• Asynchronous Regional Regression Model (ARRMv1,2)

– Uses the ARRM downscaling technique, developed by Dr. Katharine Hayhoe, Dr. Anne Stoner, Ian Scott-Fleming and colleagues at Texas Tech University.

• Includes 6 global climate models from the CMIP5 suite:– CCSM4, GFDL-ESM2G, IPSL-CM5A-LR, MIROC5, MPI-ESM-LR,

and MRI-CGCM3 (more are available for expansion).

• Downscales 2 future climate scenarios: – Lower emissions (RCP4.5) / Higher emissions (RCP8.5)

• Three daily fields are currently available for analysis: – Maximum Temperature (°C)– Minimum Temperature (°C)– Precipitation (mm)

Station-based downscaling with ARRM

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The NASA NEX-GDDP Dataset

Overview of this gridded dataset:

• 21 global climate models from the CMIP5 suite

• 1 Historical and 2 Future scenarios:

– Lower emissions (RCP4.5) / Higher emissions (RCP8.5)

• BCSD downscaling to 0.25° x 0.25° (globally gridded)

• Three daily fields:

– Maximum Temperature (°C)

– Minimum Temperature (°C)

– Precipitation (mm)

• More info: https://cds.nccs.nasa.gov/nex-gddp/

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Climate Data Exercise

• 6 CMIP5 Global Climate Models (GCMs), selected for their

ability to reproduce the Indian Monsoon and their long

development history

• 2 future Representative Concentration Pathways (RCPs)

• 3 variables: daily maximum and minimum temperature, 24

hour cumulative precipitation

• 64 out of 79 weather stations

• 2 sets of downscaled projections

Projections for individual weather stations, downscaled using

ARRMv2 (for temperature) and ARRMv1 (for precipitation)

Gridded projections covering all of India, downscaled using

NASA NEX

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Two Future Scenarios: Higher and Lower

Continued reliance on fossil fuels, but with much greater efficiency than today.

Developed nations’ emissions peak, then decline, while developing nations’ emissions growth continues.

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Two Future Scenarios: Higher and Lower

Transition to alternative energy sources.Developed nations reduce emissions ~80% by 2050Developing nations participate in emission reductions.

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90° E

90° E

80° E

80° E

70° E

70° E

30° N 30° N

20° N 20° N

10° N 10° N

64 out of 79 long-term weather stations that have sufficient daily maximum and minimum temperature and 24 hour cumulative precipitation to be downscaled

Climate Data Exercise

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SAMPLE.PLOTS -> 3 TYPES OF EXCEL PLOTS

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SAMPLE.PLOTS -> 3 TYPES OF EXCEL PLOTS

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Key Take Aways

Downscaling Methods

• The “perfect model” framework for evaluating downscaling methods consistently identifies geographic locations and quantiles at which the stationarity assumption is violated.

• For temperature, all ESDMs show reasonable stationarity in the middle of the distribution in most regions but degrade toward the tails and at high latitudes, especially for simpler methods.

• For precipitation, methods show sharp differences depending on the quantile of the distribution. This has important implications for application of ESDM output to impact assessment.

• Using the “perfect model” framework as a development tool has created an ESDM with biases at least equal to, and generally lower than, its predecessor; upcoming research will test ARRMv2 biases in precipitation and relative humidity.

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I’d like to do my own downscaling

The downscaling we have provided uses:

(1) GHCN weather station data, available here: https://www.ncdc.noaa.gov/oa/climate/ghcn-daily/

(2) 0.25 degree gridded data from the Global Meteorological Forcing Dataset, available here: http://hydrology.princeton.edu/data.pgf.php

If the spatial and/or temporal resolution of these observations meet your needs, you do not need to do any further downscaling.

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SUMMARY

Climate is changing; incorporating future projections into planning can help cities prepare for a different future.

We scientists obsess over uncertainty, but on the ground even qualitative information (direction of trend) and awareness of vulnerabilities can be useful.

The key to success is integrating climate preparedness into existing planning frameworks and mechanisms, not treating it like something new.