System for multivariate climate change forecasting with uncertainty quantification

Patent metadata

Field	Value
Publication number	US-10488556-B2
Application number	US-201515127635-A
Country	US
Kind code	B2
Filing date	Mar 27, 2015
Priority date	Mar 28, 2014
Publication date	Nov 26, 2019
Grant date	Nov 26, 2019

Abstract

A system and method for providing multivariate climate change forecasting are provided that obtain, from one or more climate model datasets, simulated historical and future climate model data, and from one or more climate observational datasets, historical observed climate data. A statistical distribution, using a Bayesian model, is provided of extremes or climate indices for one or more variable climate features using the simulated climate model data and the observed climate data. One or more metrics are determined, including a prediction of a future climate variable for a determined future time period, a confidence bound of the prediction of the future climate variable for the determined future time period, and a prediction bound for the future climate variable for the determined future time period. The metrics can be transmitted to a variety of applications in a variety of formats.

First claim

What is claimed is:

1. A system for providing multivariate climate change forecasting comprising:
one or more processors and memory, and machine-readable instructions stored in the memory that, upon execution by the one or more processors cause the system to carry out operations comprising:
obtaining, from one or more climate model datasets, climate model data comprising simulated historical climate model data used in one or more climate models and simulated future climate model data from the one or more climate models;
obtaining, from one or more climate observational datasets, observational data comprising historical observed climate data;
providing a statistical distribution of extremes or climate indices for one or more variable climate features using the climate model data and the observational model data, comprising:
sorting the climate model data and the observational climate data by numeric magnitude of extreme events or climate indices to obtain rankings,
statistically comparing rankings of the climate model data from a least some of the climate models to the rankings of the observational climate data to derive weights for each climate model simulation, and
from the weights, deriving a distribution of extreme events or climate indices over a determined time period;

determining one or more metrics from the variable climate features, each of the metrics comprising:
a prediction of a future climate variable for a determined future time period,
a confidence bound of the prediction of the future climate variable for the determined future time period, and
a prediction bound for the future climate variable for the determined future time period; and

outputting the one or more metrics to an output device.

2. The system of claim 1 , wherein the variable climate features include one or more of precipitation, precipitation extremes, temperature, wind direction, wind speed, sea surface temperature, convective available potential energy, soil moisture, crop moisture, droughts, hurricanes, heatwaves, cold snaps, tornadoes, heating degree days, cooling degree days, heatwave intensity, or coldwave intensity.

3. The system of claim 1 , wherein the climate indices include indices representing heating degree days, cooling degree days, soil moisture, precipitation, crop moisture, heatwave intensity, or coldwave intensity.

4. The system of claim 1 , further comprising determining a climate model weight for each of the one or more climate models from which the climate model data were obtained.

5. The system of claim 1 , wherein the step of providing a statistical distribution of extremes or climate indices comprises estimating framework parameters using the simulated historical climate model data and the simulated future climate model data over historical and future time periods, the parameters comprising descriptions of the variable climate features and dependences among the variable climate features.

6. The system of claim 1 , wherein the step of providing a statistical distribution of extremes or climate indices comprises:
using the climate model data and the observational climate data to obtain a distribution for each climate variable, the distribution described by a set of random unknown parameters,
providing a prior distribution for each of the random unknown parameters;
deriving posterior distributions using a Bayesian model.

7. The system of claim 1 , wherein in the step of providing a statistical distribution of extremes of climate indices, the distribution is simulated using a Markov Chain Monte Carlo computational engine.

8. The system of claim 1 , wherein the determined future time period comprises a monthly time period, a seasonal time period, an annual time period, or a multi-annual time period.

9. The system of claim 1 , wherein the metrics are determined globally or for a specified geographic region.

10. The system of claim 1 , wherein the metrics include one or more of a future distribution of precipitation, precipitation extremes, temperature, wind direction, wind speed, sea surface temperature, convective available potential energy, soil moisture, crop moisture, droughts, hurricanes, heatwaves, cold snaps, tornadoes, heating degree days, cooling degree days, heatwave intensity, or coldwave intensity for a specified geographic region, or one or more of bias parameters for any one of the climate model simulations.

11. The system of claim 1 , further comprising determining from the metrics a precipitation intensity-duration-frequency curve and outputting the precipitation intensity-duration-frequency curve to the output device.

12. The system of claim 1 , further comprising determining from the metrics precipitation extremes projections and estimating changes in rainfall events expected to be exceeded only once on average in the determined future time period.

13. The system of claim 1 , further comprising determining from the metrics probable maximum precipitation ratios for the determined future time period and an evolution of the probable maximum precipitation ratios over time.

14. The system of claim 1 , further comprising determining from the metrics probable maximum ratios for one or more of precipitation, temperature, wind direction, wind speed, sea surface temperature, convective available potential energy, soil moisture, crop moisture, droughts, hurricanes, heatwaves, cold snaps, tornadoes, heating degree days, cooling degree days, heatwave intensity, and coldwave intensity for a determined future time period and an evolution of the probable maximum ratio over time.

15. The system of claim 1 , further comprising determining from the metrics one or more of temperature change projections for a specified geographic region and precipitation extremes change projections for a specified geographic region.

16. The system of claim 1 , further comprising determining from the metrics projections of changes in one or more of precipitation, temperature, wind direction, wind speed, sea surface temperature, convective available potential energy, soil moisture, crop moisture, droughts, hurricanes, heatwaves, cold snaps, tornadoes, heating degree days, cooling degree days, heatwave intensity, and coldwave intensity for a specified geographic region.

17. The system of claim 1 , further comprising determining from the metrics climate model skill diagnostics.

18. The system of claim 1 , wherein the output device comprises a computer, a personal computer, a workstation, a server, a laptop computer, a tablet computer, a mobile telephone, a mobile computing device, a monitor, a video display device, a printer.

19. A method for providing multivariate climate change forecasting comprising:
receiving, at a computer comprising one or more processors and memory, from one or more climate model datasets, climate model data comprising simulated historical climate model data used in one or more climate models and simulated future climate model data from the one or more climate models;
receiving, at the computer, from one or more climate observational datasets, observational data comprising historical observed climate data;
providing a statistical distribution of extremes or climate indices for one or more variable climate features using the climate model data and the observational model data, comprising:
sorting the climate model data and the observational climate data by numeric magnitude of extreme events or climate indices to obtain rankings,
statistically comparing rankings of the climate model data from a least some of the climate models to

Assignees

• Univ Northeastern

Inventors

• Kodra Evan
• Ganguly Auroop R

Classifications

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G01W1/10 Primary

  Devices for predicting weather conditions (computers per se G06; display devices G09) · CPC title

• G06N7/08

  using chaos models or non-linear system models · CPC title

• G01W1/06

  giving a combined indication of weather conditions (catathermometers for measuring "cooling value" related either to weather conditions or to comfort of other human environment G01W1/17; computers per se G06) · CPC title

• G06N7/005

  Physics · mapped topic