Projected future climate: Difference between revisions
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===Downscaling=== | ===Downscaling=== | ||
To resolve some of the issues with their coarse resolution | To resolve some of the issues with their coarse resolution, raw GCM output is often ''downscaled'', or post-processed to represent more local scales--at most 30 miles/50 km, and often 7 miles/12 km and smaller. Downscaling methods fall into two general categories: ''dynamical'' or ''statistical''. In dynamical downscaling, a higher-resolution regional climate model (RCM) is run over the domain of interest, driven by boundary conditions taken from a GCM simulation. This produces localized projections that are better grounded in the physical processes at these scales, but with far higher computing costs than statistical downscaling, which is based purely on spatial statistical relationships among observed climate variables. But because statistical downscaling relies on historical relationships, it may not correctly represent localized future changes in those variables. | ||
Many different downscaled CMIP5 datasets have been developed and publicly archived, serving as the basis for both interactive plotting and analysis tools (i.e., Climate change portals) and impact studies and assessments. Reflecting the disparity in the computational costs, the vast majority of these datasets were produced through statistical downscaling. | Many different downscaled CMIP5 datasets have been developed and publicly archived, serving as the basis for both interactive plotting and analysis tools (i.e., Climate change portals) and impact studies and assessments. Reflecting the disparity in the computational costs, the vast majority of these datasets were produced through statistical downscaling. | ||
The general benefits of downscaling are that it facilitates the use of GCM data in local-impact modes (such as hydrologic models) that require higher-resolution inputs, and provides end users with projections that are at least superficially more relevant to their needs. But downscaling does necessarily not make the depiction of future change more ''accurate'' than in the underlying raw GCM projection. Different downscaling methods applied to the same GCM projection can result in quite different projected climate changes, especially in precipitation, for a given location. | |||
==Future temperature== | ==Future temperature== | ||
Revision as of 13:54, 12 May 2021
Overview
As described in Recent climate change, the climate of the Colorado River Basin has become substantially warmer in the past 40 years, very likely due to human changes to the atmosphere and climate system. Meanwhile, average precipitation in the basin has not clearly changed, but the underlying atmospheric processes (e.g., circulation patterns, water vapor content) are already being influenced by the warming global climate.
Both basic physics and our most sophisticated tools (global climate models; GCMs) tell us that further warming will occur in the basin over at least the next several decades, at a similar or greater rate than what has been observed since 1980. Neither the physics nor the GCMs present a clear picture of future precipitation change, however.
Relevance
Continued warming of the basin’s climate will further impact the surface water balance and hydrology, leading to chronic drought conditions by today's standards in the absence of a large increase in average precipitation. More warming will also further stress the basin’s ecosystems and species, potentially beyond thresholds for viability in some cases.
Methods
Global climate models
Global climate models (GCMs) are extraordinarily complex math-based software programs that simulate the Earth’s climate system. GCMs partition the Earth into thousands of 3-D gridboxes or cells, typically 30 to 80 miles (50 to 130 km) on a side horizontally, and use equations based on both observations and fundamental physical laws to represent the movement of energy, air, water, and other constituents between the gridboxes. GCMs are the main tools used to diagnose past and recent climate changes, and to generate physically plausible scenarios of the future climate. At the global scale, GCMs produce realistic simulations of key physical phenomena, broad-scale patterns, and statistical characteristics of the historical and current climate, though this realism weakens at finer scales.
Several dozen GCMs have been developed by over 20 modeling centers in 10 countries. The magnitude of projected future climate change differs among these GCMs, which reflects unresolved scientific uncertainty regarding some key climate processes and the resultant different ways that the modeling teams represent those processes in their models. A major limitation across all GCMs remains their horizontal resolution; the gridboxes are too large to reflect the complex terrain of mountainous areas such as in the Colorado River Basin, or to directly simulate processes like cloud formation or convective storms which occur at smaller, sub-grid scales. Clouds and convective storms are indirectly represented in GCMs through parameterizations: generalized values based on observations or additional modeling.
Simulations of future climate from GCMs are known as projections, as opposed to predictions or forecasts, because the projections are conditional on an assumed future trajectory for greenhouse gases and other human influences on climate. To represent the uncertainty in the future emissions of greenhouse gases, scenarios or storylines now called representative concentration pathways (RCPs) have been developed. These are labeled according to their impact on the Earth’s surface energy balance by 2100, in units of W/m2:
- RCP2.6 (low emissions)
- RCP4.5 (medium-low)
- RCP6.0 (medium)
- RCP8.5 (high emissions).
Most published analyses of climate change focus on RCP 4.5 and/or RCP8.5; RCP2.6 is considered by many experts to be implausibly optimistic in its assumed reduction of emissions.
Under the auspices of the Coupled Model Intercomparison Project (CMIP), the available GCMs are run under standardized protocols, including emissions scenarios as described above, to produce future climate projections to support the periodic Intergovernmental Panel on Climate Change (IPCC) reports. Most of the currently available GCM analyses of local impacts, e.g, for Colorado River Basin hydrology, are based on CMIP5, whose output was originally released in 2011-2012. The most recent set of projections, CMIP6, were released in 2019-2020 and are still being run through a similar chain of subsequent modeling and impact analyses as CMIP5.
Downscaling
To resolve some of the issues with their coarse resolution, raw GCM output is often downscaled, or post-processed to represent more local scales--at most 30 miles/50 km, and often 7 miles/12 km and smaller. Downscaling methods fall into two general categories: dynamical or statistical. In dynamical downscaling, a higher-resolution regional climate model (RCM) is run over the domain of interest, driven by boundary conditions taken from a GCM simulation. This produces localized projections that are better grounded in the physical processes at these scales, but with far higher computing costs than statistical downscaling, which is based purely on spatial statistical relationships among observed climate variables. But because statistical downscaling relies on historical relationships, it may not correctly represent localized future changes in those variables.
Many different downscaled CMIP5 datasets have been developed and publicly archived, serving as the basis for both interactive plotting and analysis tools (i.e., Climate change portals) and impact studies and assessments. Reflecting the disparity in the computational costs, the vast majority of these datasets were produced through statistical downscaling.
The general benefits of downscaling are that it facilitates the use of GCM data in local-impact modes (such as hydrologic models) that require higher-resolution inputs, and provides end users with projections that are at least superficially more relevant to their needs. But downscaling does necessarily not make the depiction of future change more accurate than in the underlying raw GCM projection. Different downscaling methods applied to the same GCM projection can result in quite different projected climate changes, especially in precipitation, for a given location.
Future temperature
Future precipitation
As discussed in Climate patterns and variability, annual precipitation in the basin is highly variable, and there are no recent trends in annual or seasonal precipitation that clearly emerge from the background “noise” of historical year-to-year and decadal variability, as with temperature. But the most recent two decades does stand out for overall dryness, which is accentuated by the preceding two wet decades in the 1980s and 1990s (Figures 3 & 4). By slight margins, 2000-2020 has been the driest 21-year period on record in both the Upper Basin (with 94% of the 20th-century average) and Lower Basin (88% of the 20th-century average). The period since 2000 also includes the driest single water years on record in the Upper Basin (2018) and Lower Basin (2002).
Drought indicators
Other climate metrics
Data and tools
There are several climate tools that are useful for plotting and examining time-series and recent trends in temperature, precipitation, and other climate variables over specific areas (states, counties, river basins, etc.). Each tool depicts one or more Gridded climate datasets (LINK), so it is advised that users of the tools familiarize themselves with these datasets as well.
NOAA NCEI Climate at a Glance - Regional Time Series
The “CAG” tool is a versatile tool that can be used to generate many types of charts, maps, and analyses from NOAA’s official nClimGrid monthly gridded climate dataset. The link above opens the Regional and Time Series options, allowing users to plot temperature, precipitation, and other variables for the Upper Basin, Lower Basin, or many other U.S. basins and regions. All variables can be plotted from 1895 to present.
Additional resources
State of the Science Report
Chapter 2 of the State of the Science report describes recent climate changes in greater detail, in section 2.10.
NCA4 Climate Science Special Report
The 2018 Climate Science Special Report (CSSR), Volume 1 of the Fourth National Climate Assessment (NCA4), describes the historical record and likely causes of recent temperature change (Ch 6.1, 6.2) and recent precipitation change (Ch. 7.1) in the U.S.
Research directions
New and Notable Research (2020-present)
[<URL FOR PAPER>]
Summary