Hemispheric and large‐scale land‐surface air temperature variations: An extensive revision and an update to 2010

Jones, Phil; Lister, David; Harpham, Colin; Salmon, M.; Morice, Colin

doi:10.1029/2011jd017139

Cited by 761 publications

(745 citation statements)

References 36 publications

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“…However, higher latitude has warmed more than lower latitude over the past half century (IPCC 2013;Jones et al 2012). Therefore, at high latitude, the contribution of stronger warming to advancing trend in spring phenophases can be partly or totally offset by the weaker temperature sensitivity there.…”

Section: Discussionmentioning

confidence: 99%

Geographical pattern in first bloom variability and its relation to temperature sensitivity in the USA and China

Wang

Dai

et al. 2014

Int J Biometeorol

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Advance in spring plant phenology over the last several decades has been found in all continents of the Northern Hemisphere. Compared to the studies detecting phenological trends, the studies investigating the geographical pattern of phenological variability (including mean date and magnitude of variability) are rather limited. In this study, we analyzed spatial pattern of mean date and standard deviation (SD) of first bloom date (FBD) time series (≥15 years) for black locust (Robinia pseudoacacia) at 22 stations in China, common lilac (Syringa vulgaris) at 79 stations in the Western US and Chinese lilac (Syringa chinensis) at 45 stations in the Eastern US. Subsequently, the impact of geographical factors (latitude, longitude, and altitude) on the mean date and SD was quantified by using the multiple regression analysis method. Meanwhile, the relationship between FBD variability and temperature sensitivity of FBD was examined. Results showed that the mean FBD highly depended on geographical factors for all the three species. Compared to the mean date, the dependence of SD of FBD time series on geographical factors was weaker. The geographical factors could only explain 13 to 31 % of spatial variance in SD of FBD. The negative regression coefficients of latitude (P < 0.05 except black locust) indicated that FBD is more variable at lower latitude. At most of stations, significant and negative correlations between FBD and preseason temperature on interannual scale were found, but the temperature sensitivity varied among different stations. The magnitude of temperature sensitivity decreased with increasing latitude. In general, the locations at lower latitude had earlier and more variable spring phenophase and showed stronger phenological response to climate change than the locations at higher latitude.

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Section: Discussionmentioning

confidence: 99%

Geographical pattern in first bloom variability and its relation to temperature sensitivity in the USA and China

Wang

Dai

et al. 2014

Int J Biometeorol

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“…Throughout this book, we relate ΔRF computed in this manner to ΔT relative to a pre-industrial baseline of 1850-1900. This mismatch of baseline values for ΔRF and ΔT is a consequence of the IPCC precedent of initializing ΔRF in 1750 combined with 1850 marking the first thermometer based estimate of GMST provided by the Climate Research Unit of East Anglia, UK (Jones et al 2012). The rise in RF of climate between 1750 and 1900 was small, so the mismatch of baselines has no significant influence on our analysis.…”

Section: Methodsmentioning

confidence: 89%

“…2.13, which show the best estimate (symbols) and range of AAWR (error bars) that is found for each of the six OHC records. The three groupings of data points show AAWR found using ΔT from CRU (Jones et al 2012), GISS (Hansen et al 2010), and NCEI (Karl et al 2015). Nearly identical values of AAWR are found, regardless of which data center record is used to define ΔT.…”

Section: Attributable Anthropogenic Warming Ratementioning

confidence: 99%

Forecasting Global Warming

et al. 2017

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This chapter provides an overview of the factors that will govern the rise in global mean surface temperature (GMST) over the rest of this century. We evaluate GMST using two approaches: analysis of archived output from atmospheric, oceanic general circulation models (GCMs) and calculations conducted using a computational framework developed by our group, termed the Empirical Model of Global Climate (EM-GC). Comparison of the observed rise in GMST over the past 32 years with GCM output reveals these models tend to warm too quickly, on average by about a factor of two. Most GCMs likely represent climate feedback in a manner that amplifies the radiative forcing of climate due to greenhouse gases (GHGs) too strongly. The GCM-based forecast of GMST over the rest of the century predicts neither the target (1.5 °C) nor upper limit (2.0 °C warming) of the Paris Climate Agreement will be achieved if GHGs follow the trajectories of either the Representative Concentration Pathway (RCP) 4.5 or 8.5 scenarios. Conversely, forecasts of GMST conducted in the EM-GC framework indicate that if GHGs follow the RCP 4.5 trajectory, there is a reasonably good probability (~75 %) the Paris target of 1.5 °C warming will be achieved, and an excellent probability (>95 %) global warming will remain below 2.0 °C. Uncertainty in the EM-GC forecast of GMST is primarily caused by the ability to simulate past climate for various combinations of parameters that represent climate feedback and radiative forcing due to aerosols, which provide disparate projections of future warming.

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“…The Climatic Research Unit 5×5°gridded instrumental surface air temperature dataset (CRUTEM4v) for the period AD 1920-1999 (Norwich, UK; www.cru.uea.ac.uk/cru/data/ temperature; Jones et al 2012) was used in this study. Based on the spatial distribution of available proxy records, the reconstructed area of eastern and south-central Asia was here defined as the area bounded by 0°N-60°N and 60°E-145°E.…”

Section: Instrumental Datamentioning

confidence: 99%

A multi-proxy reconstruction of spatial and temporal variations in Asian summer temperatures over the last millennium

Shi

Yang

et al. 2015

Climatic Change

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To investigate climate variability in Asia during the last millennium, the spatial and temporal evolution of summer (June-July-August; JJA) temperature in eastern and south-central Asia is reconstructed using multi-proxy records and the regularized expectation maximization (RegEM) algorithm with truncated total least squares (TTLS), under a point-by-point regression (PPR) framework. The temperature index reconstructions show that the late 20th century was the Institute of Geography, Russian Academy of Sciences, Moscow, Russia warmest period in Asia over the past millennium. The temperature field reconstructions illustrate that temperatures in central, eastern, and southern China during the 11th and 13th centuries, and in western Asia during the 12th century, were significantly higher than those in other regions, and comparable to levels in the 20th century. Except for the most recent warming, all identified warm events showed distinct regional expressions and none were uniform over the entire reconstruction area. The main finding of the study is that spatial temperature patterns have, on centennial timescales, varied greatly over the last millennium. Moreover, seven climate model simulations, from the Coupled Model Intercomparison Project Phase 5 (CMIP5), over the same region of Asia, are all consistent with the temperature index reconstruction at the 99 % confidence level. Only spatial temperature patterns extracted as the first empirical orthogonal function (EOF) from the GISS-E2-R and MPI-ESM-P model simulations are significant and consistent with the temperature field reconstruction over the past millennium in Asia at the 90 % confidence level. This indicates that both the reconstruction and the simulations depict the temporal climate variability well over the past millennium. However, the spatial simulation or reconstruction capability of climate variability over the past millennium could be still limited. For reconstruction, some grid points do not pass validation tests and reveal the need for more proxies with high temporal resolution, accurate dating, and sensitive temperature signals, especially in central Asia and before AD 1400.

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Hemispheric and large‐scale land‐surface air temperature variations: An extensive revision and an update to 2010

Cited by 761 publications

References 36 publications

Geographical pattern in first bloom variability and its relation to temperature sensitivity in the USA and China

Geographical pattern in first bloom variability and its relation to temperature sensitivity in the USA and China

Forecasting Global Warming

A multi-proxy reconstruction of spatial and temporal variations in Asian summer temperatures over the last millennium

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