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Quantifying climate change in the presence of natural variability requires highly accurate global measurements covering more than a decade. Instrument design considerations for trending terrestrial emitted radiance are described.
Forcing due to solar and volcanic variability, on the natural side, and greenhouse gas and aerosol emissions, on the anthropogenic side, are the main inputs to climate models. Reliable climate model simulations of past and future climate change depend crucially upon them. Here we analyze large ensembles of simulations using a comprehensive Earth System Model to quantify uncertainties in global climate change attributable to differences in prescribed forcings. The different forcings considered here are those used in the two most recent phases of the Coupled Model Intercomparison Project (CMIP), namely CMIP5 and CMIP6. We show significant differences in simulated global surface air temperature due to volcanic aerosol forcing in the second half of the 19th century and in the early 21st century. The latter arise from small-to-moderate eruptions incorporated in CMIP6 simulations but not in CMIP5 simulations. We also find significant differences in global surface air temperature and Arctic sea ice area due to anthropogenic aerosol forcing in the second half of the 20th century and early 21st century. These differences are as large as those obtained in different versions of an Earth System Model employing identical forcings. In simulations from 2015 to 2100, we find significant differences in the rates of projected global warming arising from CMIP5 and CMIP6 concentration pathways that differ slightly but are equivalent in terms of their nominal radiative forcing levels in 2100. Our results highlight the influence of assumptions about natural and anthropogenic aerosol loadings on carbon budgets, the likelihood of meeting Paris targets, and the equivalence of future forcing scenarios.
This study examines the nature of boreal summer subseasonal atmospheric variability based on the new NASA Modern-Era Retrospective analysis for Research and Applications (MERRA) for the period 1979-2010. An analysis of the June, July and August subseasonal 250hPa v-wind anomalies shows distinct Rossby wave-like structures that appear to be guided by the mean jets. On monthly subseasonal time scales, the leading waves (the first 10 rotated empirical orthogonal functions or REOFs of the 250hPa v-wind) explain about 50% of the Northern Hemisphere vwind variability, and account for more than 30% (60%) of the precipitation (surface temperature) variability over a number of regions of the northern middle and high latitudes, including the U.S. northern Great Plains, parts of Canada, Europe, and Russia. The first REOF in particular, consists of a Rossby wave that extends across northern Eurasia where it is a dominant contributor to monthly surface temperature and precipitation variability, and played an important role in the 2003 European and 2010 Russian heat waves. While primarily subseasonal in nature, the Rossby waves can at times have a substantial seasonal mean component. This is exemplified by REOF 4 which played a major role in the development of the most intense anomalies of the U.S. 1988 drought (during June) and the 1993 flooding (during July), though differed in the latter event by also making an important contribution to the seasonal mean anomalies. A stationary wave model (SWM) is used to reproduce some of the basic features of the observed waves and provide insight into the nature of the forcing. In particular, the responses to a set of idealized forcing functions are used to map the optimal forcing patterns of the leading waves. Also, experiments to reproduce the observed waves with the SWM using MERRA-based estimates of the forcing indicate that the wave forcing is dominated by sub-monthly vorticity transients.
Wind energy is a virtually carbon-free and pollution-free electricity source, with global wind resources greatly exceeding electricity demand. Accordingly, the installed capacity of wind turbines grew at an annualized rate of >20% from 2000 to 2019 and is projected to increase by a further 50% by the end of 2023. Here, in this Review, we describe the factors that dictate the wind resource magnitude and variability and illustrate the tools and techniques that are being used to make projections of wind resources and wind turbine operating conditions. Natural variability due to the action of internal climate modes appears to dominate over global-warming-induced non-stationarity over most areas with large wind energy installations or potential. However, there is evidence for increased wind energy resources by the end of the current century in northern Europe and the US Southern Great Plains. New technology trends are changing the sensitivity of wind energy to global climate non-stationarity and, thus, present new challenges and opportunities for innovative research. The evolution of climate modelling to increasingly address mesoscale processes is providing improved projections of both wind resources and wind turbine operating conditions, and will contribute to continued reductions in the levelized cost of energy from wind power generation.
Climate-induced changes in hypoxia are among the most serious threats facing estuaries, which are among the most productive ecosystems on Earth. Future projections of estuarine hypoxia typically involve long-term multi-decadal continuous simulations or more computationally efficient time slice and delta methods that are restricted to short historical and future periods. We make a first comparison of these three methods by applying a linked terrestrial–estuarine model to the Chesapeake Bay, a large coastal-plain estuary in the eastern United States. Results show that the time slice approach accurately captures the behavior of the continuous approach, indicating a minimal impact of model memory. However, increases in mean annual hypoxic volume by the mid-twenty-first century simulated by the delta approach (+ 19%) are approximately twice as large as the time slice and continuous experiments (+ 9% and + 11%, respectively), indicating an important impact of changes in climate variability. Our findings suggest that system memory and projected changes in climate variability, as well as simulation length and natural variability of system hypoxia, should be considered when deciding to apply the more computationally efficient delta and time slice methods.
This research compared needle litter decay rates and underlying mechanisms between litter released from insect-killed trees to that of naturally senesced litter from healthy trees across climate variables with elevation and snowmelt rates as variables. Our research was designed to address questions of whether there is a significant impact to C and N cycling at the terrestrial-atmospheric interface in association with pine needle decomposition and the role of changes in soil moisture and temperature as a function of these processes. Field activities were primarily conducted within the Rocky Mountain Biological Laboratory’s (RMBL) East River watershed across a spectrum of ecoregions that spanned approximately 700 vertical meters. Forested catchments at these elevations in southwestern Colorado are threatened by heat-induced drought, altered snowmelt patterns, and invasive insect infestation that have all been associated with climate change. Needle litter from two members of the Pinaceae family experiencing large-scale insect infestations (spruce and lodgepole pine) were collected, redeployed and then sampled over the course of three years. We experimentally tested three tractable parameters that are known to impact litter decay and biogeochemical cycling and are relevant to bark beetle-induced tree mortality: (1) moisture content, (2) temperature by using elevation as a surrogate, and (3) changes to litter compositional chemistry that result from beetle infestation. We examined decay processes and monitored the migration of soluble and gaseous nitrogen and carbon species as well as microbiological signatures of decay such as biodegradable fractions, metal availability and the temporal succession of associated microorganisms at the terrestrial interface.
Marine heatwaves (MHWs)—extremely warm, persistent sea surface temperature (SST) anomalies causing substantial ecological and economic consequences—have increased worldwide in recent decades. Concurrent increases in global temperatures suggest that climate change impacted MHW occurrences, beyond random changes arising from natural internal variability. Moreover, the long-term SST warming trend was not constant but instead had more rapid warming in recent decades. Here we show that this nonlinear trend can—on its own—appear to increase SST variance and hence MHW frequency. Using a Linear Inverse Model to separate climate change contributions to SST means and internal variability, both in observations and CMIP6 historical simulations, we find that most MHW increases resulted from regional mean climate trends that alone increased the probability of SSTs exceeding a MHW threshold. Our results suggest the need to carefully attribute global warming-induced changes in climate extremes, which may not always reflect underlying changes in variability.
This paper focuses on how fresh water and radiative fluxes over the tropical oceans change during ENSO warm and cold events and how these changes affect the tropical energy balance. At present, ENSO remains the most prominent known mode of natural variability at interannual time scales. While this natural perturbation to climate is quite distinct from possible anthropogenic changes in climate, adjustments in the tropical water and energy budgets during ENSO may give insight into feedback processes involving water vapor and cloud feedbacks. Although great advances have been made in understanding this phenomenon and realizing prediction skill over the past decade, our ability to document the coupled water and energy changes observationally and to represent them in climate models seems far from settled (Soden, 2000 J Climate). In a companion paper we have presented observational analyses, based principally on space-based measurements which document systematic changes in rainfall, evaporation, and surface and top-of-atmosphere (TOA) radiative fluxes. Here we analyze several contemporary climate models run with observed SSTs over recent decades and compare SST-induced changes in radiation, precipitation, evaporation, and energy transport to observational results. Among these are the NASA / NCAR Finite Volume Model, the NCAR Community Climate Model, the NCEP Global Spectral Model, and the NASA NSIPP Model. Key disagreements between model and observational results noted in the recent literature are shown to be due predominantly to observational shortcomings. A reexamination of the Langley 8-Year Surface Radiation Budget data reveals errors in the SST surface longwave emission due to biased SSTs. Subsequent correction allows use of this data set along with ERBE TOA fluxes to infer net atmospheric radiative heating. Further analysis of recent rainfall algorithms provides new estimates for precipitation variability in line with interannual evaporation changes inferred from the da Silva, Young, Levitus COADS analysis. The overall results from our analysis suggest an increase (decrease) of the hydrologic cycle during ENSO warm (cold) events at the rate of about 5 W/sq m per K of SST change. Model results agree reasonably well with this estimate of sensitivity. This rate is slightly less than that which would be expected for constant relative humidity over the tropical oceans. There remain, however, significant quantitative uncertainties in cloud forcing changes in the models as compared to observations. These differences are examined in relationship to model convection and cloud parameterizations Analysis of the possible sampling and measurement errors compared to systematic model errors is also presented.
Evapotranspiration (ET) is a climatic variable critical to the hydrologic cycle and is used to evaluate spatiotemporal trends in drought conditions. Although in-situ observations provide accurate ET information, these records are spatially sparse. The United States Department of Agriculture (USDA) Midwest Climate Hub, National Integrated Drought Information System, Minnesota Department of Agriculture, Michigan State University, and the USDA Foreign Agricultural Service have partnered with DEVELOP to gain new insights on spatiotemporal patterns of ET with NASA satellite data. This project evaluates the feasibility of using remotely sensed ET data products to understand trends from 2001 through 2020. Actual ET (aET) data were sourced from NASA’s Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and reference ET (refET) and precipitation data were sourced from the Gridded Surface Meteorological Dataset (gridMET). Spatial and temporal differences in the MODIS and gridMET data sets were resolved such that gridded data could be compared. These data sets were then used to produce monthly Normals maps of mm/8-day of precipitation, aET, and refET. Using precipitation and ET Normals maps, hydrologic state maps were produced by subtracting either refET or aET from precipitation. These hydrologic state maps provide a proxy water balance by summarizing the difference between water entering and leaving the ground surface. Additionally, the ET products were compared in timeseries plots. The ET products from this project provided partners with comparable datasets to assess potential drought and flooding conditions to support Midwest agricultural and natural resource managers in decision-making.
There is overwhelming evidence that forest trees are locally adapted to climate. Thus, genecological models based on population phenotypes have been used to measure local adaptation, infer genetic maladaptation to climate, and guide assisted migration. However, instead of phenotypes, there is increasing interest in using genomic data for gene resource management. We used whole-genome resequencing and common-garden experiments to understand the genetic architecture of adaptive traits in black cottonwood. We studied the potential of using genome-wide association studies (GWAS) and genomic prediction to detect causal loci, identify climate-adapted phenotypes, and inform gene resource management. We analyzed population structure by partitioning phenotypic and genomic (single-nucleotide polymorphism) variation among 840 genotypes collected from 91 stands along 16 rivers. Most phenotypic variation (60 to 81%) occurred among populations and was strongly associated with climate. Population phenotypes were predicted well using genomic data (e.g., predictive abilityr> 0.9) but almost as well using climate or geography (r> 0.8). In contrast, genomic prediction within populations was poor (r< 0.2). We identified many GWAS associations among populations, but most appeared to be spurious based on pooled within-population analyses. Hierarchical partitioning of linkage disequilibrium and haplotype sharing suggested that within-population genomic prediction and GWAS were poor because allele frequencies of causal loci and linked markers differed among populations. Given the urgent need to conserve natural populations and ecosystems, our results suggest that climate variables alone can be used to predict population phenotypes, delineate seed zones and deployment zones, and guide assisted migration.
Our paradigm-shifting framework will apply machine learning and perform physical analysis of contemporary volcanoes to develop sound forecasts of extreme eruptions in the 21stcentury and their abrupt drying and cooling impacts on the warming climate. We will also bridge climate data driven regression models and earth system model output to trace teleconnections that exacerbate regional impacts such as Arctic amplification and western US droughts.
Various manifestations of solar activity cause disturbances known as space weather effects in the interplanetary space, near-Earth environment, and all the Earth's "spheres. Longterm variations in the frequency, intensity and relative importance of the manifestations of solar activity are due to the slow changes in the output of the solar dynamo, and they define space climate. Space climate governs long-term variations in geomagnetic activity and is the primary natural driver of terrestrial climate. To understand how the variable solar activity affects the Earth's environment, geomagnetic activity and climate on both short and long time scales, we need to understand the origins of solar activity itself and its different manifestations, as well as the sequence of coupling processes linking various parts of the system. This session provides a forum to discuss the chain of processes and relations from the Sun to the Earth's surface: the origin and long-term and short-term evolution of solar activity, initiation and temporal variations in solar flares, CMEs, coronal holes, the solar wind and its interaction with the terrestrial magnetosphere, the ionosphere and its connection to the neutral dominated regions below and the plasma dominated regions above, the stratosphere, its variations due to the changing solar activity and its interactions with the underlying troposphere, and the mechanisms of solar influences on the lower atmosphere on different time-scales. Particularly welcome are papers highlighting the coupling processes between the different domains in this complex system.
Megageomorphology is the study of regional topographic features and their relations to independent geomorphic variables that operate at the regional scale. These independent variables can be classified as either tectonic or climatic in nature. Quantitative megageomorphology stresses the causal relations between plate tectonic factors and landscape features or correlations between climatic factors and geomorphic processes. In addition, the cumulative effects of tectonics and climate on landscape evolution that simultaneously operate in a complex system of energy transfer is of interst. Regional topographic differentiation, say between continents and ocean floors, is largely the result of the different densities and density contrasts within the oceanic and continental lithosphere and their isostatic consequences. Regional tectonic processes that alter these lithospheric characteristics include rifting, collision, subduction, transpression and transtension.
The existence of global warming this century is no longer an issue of scientific debate. But there are many important questions about the nature and causes of long-term climate change, th roles of nature and human-made climate forcings and unforced (chaotic) climate variability, the practical impacts of climate change, and what, if anything, should be done to reduce global warming, Global warming is not a uniform increase of temperature, but rather involves at complex geographically varying climate change. Understanding of global warming will require improved observations of climate change itself and the forcing factors that can lead to climate change. The NASA Terra mission and other NASA Earth Science missions will provide key measurement of climate change and climate forcings. The strategy to develop an understanding of the causes and predictability of long-term climate change must be based on combination of observations with models and analysis. The upcoming NASA missions will make important contributions to the required observations.
Many geophysical variables including temperature, clouds, and precipitation exhibit robust diurnal cycles in response to the daily cycle of solar insolation. Due to the fundamental nature of this variability, it is critical that weather and climate models accurately represent the diurnal cycle. Numerical model, however, have difficulty reproducing this observed diurnal cycle behavior, which leads to systematic errors in model representation of earth energy budget terms: including TOA and surface radiation, precipitation, and surface latent and sensible heat fluxes. This study evaluates the regional diurnal cycle in the Tropics within reanalysis models (ERA-Interim and MERRA) and quantifies systematic errors in the simulated TOA flux and precipitation due to biases in the diurnal cycle representation. The focus of this study is to quantify the importance of diurnal cycle simulation to systematic bias in reanalysis data set climatologies.
Understanding and forecasting Tropical Pacific Decadal-scale Variability (TPDV) strongly rely on climate model simulations. Using a Linear Inverse Modeling (LIM) diagnostic approach, we reveal Coupled Model Intercomparison Project Phase 6 models have significant challenges in reproducing the spatial structure and dominant mechanisms of TPDV. Specifically, while the models' ensemble mean pattern of TPDV resembles that of observations, the spread across models is very large and most models show significant differences from observations. In observations, removing the coupling between extratropics and tropics reduces TPDV by ~60%–70%, and removing the tropical thermocline variability makes the central tropical Pacific a key center of action for TPDV and El Niño Southern Oscillation variability. These characteristics are only confirmed in a subset of models. Differences between observations and simulations are outside the range of natural internal TPDV noise and pose important questions regarding our ability to model the impacts of natural internal low-frequency variability superimposed on long-term climate change.
The Climate Absolute Radiance and Refractivity Observatory (CLARREO) mission addresses the need to observe high-accuracy, long-term climate change trends and to use decadal change observations as the most critical method to determine the accuracy of climate change. One of the major objectives of CLARREO is to advance the accuracy of SI traceable absolute calibration at infrared and reflected solar wavelengths. This advance is required to reach the on-orbit absolute accuracy required to allow climate change observations to survive data gaps while remaining sufficiently accurate to observe climate change to within the uncertainty of the limit of natural variability. While these capabilities exist at NIST in the laboratory, there is a need to demonstrate that it can move successfully from NIST to NASA and/or instrument vendor capabilities for future spaceborne instruments. The current work describes the test plan for the Solar, Lunar for Absolute Reflectance Imaging Spectroradiometer (SOLARIS) which is the calibration demonstration system (CDS) for the reflected solar portion of CLARREO. The goal of the CDS is to allow the testing and evaluation of calibration approaches , alternate design and/or implementation approaches and components for the CLARREO mission. SOLARIS also provides a test-bed for detector technologies, non-linearity determination and uncertainties, and application of future technology developments and suggested spacecraft instrument design modifications. The end result of efforts with the SOLARIS CDS will be an SI-traceable error budget for reflectance retrieval using solar irradiance as a reference and methods for laboratory-based, absolute calibration suitable for climate-quality data collections. The CLARREO mission addresses the need to observe high-accuracy, long-term climate change trends and advance the accuracy of SI traceable absolute calibration. The current work describes the test plan for the SOLARIS which is the calibration demonstration system for the reflected solar portion of CLARREO. SOLARIS provides a test-bed for detector technologies, non-linearity determination and uncertainties, and application of future technology developments and suggested spacecraft instrument design modifications. The end result will be an SI-traceable error budget for reflectance retrieval using solar irradiance as a reference and methods for laboratory-based, absolute calibration suitable for climate-quality data collections.
This study introduces a unique approach to estimating a fundamental but unmeasurable property of Nature: ρo 2 , the fraction of the variance of a given meteorological variable that is determined by boundary forcing (specifically, SSTs) as opposed to chaotic atmospheric dynamics. Here, we focus on continental monthly-averaged near-surface air temperature, or T2M. Rather than determining the fractions of SST-explained T2M variance found for a specific atmospheric general circulation model (AGCM) and then assuming that these fractions also represent Nature, we perform an analysis that entails: (i) evaluating a specific AGCM’s ability to capture observed variability with an infinite number of ensemble members, (ii) quantifying the degree to which different modeling systems agree on SST-land teleconnections, and (iii) combining these results into our novel estimates of ρo 2 . The derived ρo 2 fields suggest, with caveats, that AGCMs tend to overestimate the fraction of explained T2M variance (the signal-to-total variance ratio) in the Tropics.