Emerging local warming signals in observational data

Mahlstein, Irina; Hegerl, Gabriele C.; Solomon, Susan

doi:10.1029/2012gl053952

Cited by 81 publications

(81 citation statements)

References 23 publications

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“…To identify the location where models project statistically significant changes, we analyze the ratio of the mean change to variability; this is accomplished by dividing the mean changes over 20-year time periods by the standard deviation over the current climate (1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000) and shown in terms of standard deviation units (e.g., Mahlstein et al, 2012;Tebaldi et al, 2011). Previous studies have shown that the spatial and temporal scale used to define these changes can determine whether these signals are statistically significant (Lombardozzi et al, 2014).…”

Section: Model Future Projection Analysismentioning

confidence: 99%

Projections of leaf area index in earth system models

et al. 2016

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Abstract. The area of leaves in the plant canopy, measured as leaf area index (LAI), modulates key landatmosphere interactions, including the exchange of energy, moisture, carbon dioxide (CO 2 ), and other trace gases and aerosols, and is therefore an essential variable in predicting terrestrial carbon, water, and energy fluxes. Here our goal is to characterize the LAI projections from the latest generation of earth system models (ESMs) for the Representative Concentration Pathway (RCP) 8.5 and RCP4.5 scenarios. On average, the models project increases in LAI in both RCP8.5 and RCP4.5 over most of the globe, but also show decreases in some parts of the tropics. Because of projected increases in variability, there are also more frequent periods of low LAI across broad regions of the tropics. Projections of LAI changes varied greatly among models: some models project very modest changes, while others project large changes, usually increases. Modeled LAI typically increases with modeled warming in the high latitudes, but often decreases with increasing local warming in the tropics. The models with the most skill in simulating current LAI in the tropics relative to satellite observations tend to project smaller increases in LAI in the tropics in the future compared to the average of all the models. Using LAI projections to identify regions that may be vulnerable to climate change presents a slightly different picture than using precipitation projections, suggesting LAI may be an additional useful tool for understanding climate change impacts. Going forward, users of LAI projections from the CMIP5 ESMs evaluated here should be aware that model outputs do not exhibit clear-cut relationships to vegetation carbon and precipitation. Our findings underscore the need for more attention to LAI projections, in terms of understanding the drivers of projected changes and improvements to model skill.

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Section: Model Future Projection Analysismentioning

confidence: 99%

Projections of leaf area index in earth system models

et al. 2016

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“…Previous studies exploring the concept of emergence have largely focused on physical state variables of the atmosphere and ocean, such as temperature, precipitation and sea level (e.g., Diffenbaugh and Scherer, 2011;Hawkins and Sutton, 2012;Mahlstein et al, 2012;Mora et al, 2013;Lyu et al, 2014). Mora et al (2013), for example argued that the tropics, which hold the worlds greatest diversity of marine species, will exhibit emergence in ocean warming 10 years earlier than any of the other global ocean regions.…”

Section: Introductionmentioning

confidence: 99%

Emergence of multiple ocean ecosystem drivers in a large ensemble suite with an Earth system model

Rodgers

Lin

Frölicher³

2015

Biogeosciences

180

162

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Abstract. Marine ecosystems are increasingly stressed by human-induced changes. Marine ecosystem drivers that contribute to stressing ecosystems -including warming, acidification, deoxygenation and perturbations to biological productivity -can co-occur in space and time, but detecting their trends is complicated by the presence of noise associated with natural variability in the climate system. Here we use large initial-condition ensemble simulations with an Earth system model under a historical/RCP8.5 (representative concentration pathway 8.5) scenario over 1950-2100 to consider emergence characteristics for the four individual and combined drivers. Using a 1-standard-deviation (67 % confidence) threshold of signal to noise to define emergence with a 30-year trend window, we show that ocean acidification emerges much earlier than other drivers, namely during the 20th century over most of the global ocean. For biological productivity, the anthropogenic signal does not emerge from the noise over most of the global ocean before the end of the 21st century. The early emergence pattern for sea surface temperature in low latitudes is reversed from that of subsurface oxygen inventories, where emergence occurs earlier in the Southern Ocean. For the combined multiple-driver field, 41 % of the global ocean exhibits emergence for the 2005-2014 period, and 63 % for the 2075-2084 period. The combined multiple-driver field reveals emergence patterns by the end of this century that are relatively high over much of the Southern Ocean, North Pacific, and Atlantic, but relatively low over the tropics and the South Pacific. For the case of two drivers, the tropics including habitats of coral reefs emerges earliest, with this driven by the joint effects of acidification and warming. It is precisely in the regions with pronounced emergence characteristics where marine ecosystems may be expected to be pushed outside of their comfort zone determined by the degree of natural background variability to which they are adapted. The results underscore the importance of sustained multi-decadal observing systems for monitoring multiple ecosystems drivers.

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“…However, meaningful adherence to this simple principle can be challenging, and in practice researchers commonly encounter datasets for which uncertainty information is generic, misleading, or absent. Climate data records (CDRs) are not immune to this problem, despite the fact that climatic signals are usually subtle (e.g., Kennedy, 2014;Mahlstein et al, 2012;Flannaghan et al, 2014;Barnett et al, 2005), which adds to the importance of rigorous uncertainty characterization in CDRs (e.g., Immler et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Uncertainty information in climate data records from Earth observation

Merchant

Paul

Popp

et al. 2017

Earth Syst. Sci. Data

136

112

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Abstract. The question of how to derive and present uncertainty information in climate data records (CDRs) has received sustained attention within the European Space Agency Climate Change Initiative (CCI), a programme to generate CDRs addressing a range of essential climate variables (ECVs) from satellite data. Here, we review the nature, mathematics, practicalities, and communication of uncertainty information in CDRs from Earth observations. This review paper argues that CDRs derived from satellite-based Earth observation (EO) should include rigorous uncertainty information to support the application of the data in contexts such as policy, climate modelling, and numerical weather prediction reanalysis. Uncertainty, error, and quality are distinct concepts, and the case is made that CDR products should follow international metrological norms for presenting quantified uncertainty. As a baseline for good practice, total standard uncertainty should be quantified per datum in a CDR, meaning that uncertainty estimates should clearly discriminate more and less certain data. In this case, flags for data quality should not duplicate uncertainty information, but instead describe complementary information (such as the confidence in the uncertainty estimate provided or indicators of conditions violating the retrieval assumptions). The paper discusses the many sources of error in CDRs, noting that different errors may be correlated across a wide range of timescales and space scales. Error effects that contribute negligibly to the total uncertainty in a single-satellite measurement can be the dominant sources of uncertainty in a CDR on the large space scales and long timescales that are highly relevant for some climate applications. For this reason, identifying andPublished by Copernicus Publications. 512 C. J. Merchant et al.: Uncertainty information in climate data records characterizing the relevant sources of uncertainty for CDRs is particularly challenging. The characterization of uncertainty caused by a given error effect involves assessing the magnitude of the effect, the shape of the error distribution, and the propagation of the uncertainty to the geophysical variable in the CDR accounting for its error correlation properties. Uncertainty estimates can and should be validated as part of CDR validation when possible. These principles are quite general, but the approach to providing uncertainty information appropriate to different ECVs is varied, as confirmed by a brief review across different ECVs in the CCI. User requirements for uncertainty information can conflict with each other, and a variety of solutions and compromises are possible. The concept of an ensemble CDR as a simple means of communicating rigorous uncertainty information to users is discussed. Our review concludes by providing eight concrete recommendations for good practice in providing and communicating uncertainty in EO-based climate data records.

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Emerging local warming signals in observational data

Cited by 81 publications

References 23 publications

Projections of leaf area index in earth system models

Projections of leaf area index in earth system models

Emergence of multiple ocean ecosystem drivers in a large ensemble suite with an Earth system model

Uncertainty information in climate data records from Earth observation

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