Jianbin Huang

Comparison of Monthly Temperature Extremes Simulated by CMIP3 and CMIP5 Models

AbstractThe extreme monthly-mean temperatures simulated by 28 models in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) are evaluated and compared with those from 24 models in the third phase of the Coupled Model Intercomparison Project (CMIP3). Comparisons with observations and reanalyses indicate that the models from both CMIP3 and CMIP5 perform well in simulating temperature extremes, which are expressed as 20-yr return values. When the climatological annual cycle is removed, the ensemble spread in CMIP5 is smaller than that in CMIP3. Benefitting from a higher resolution, the CMIP5 models perform better at simulating extreme temperatures on the local gridcell scale. The CMIP5 representative concentration pathway (RCP4.5) and CMIP3 B1 experiments project a similar change pattern in the near future for both warm and cold extremes, and the pattern is in agreement with that of the seasonal extremes. By the late twenty-first century, the changes in monthly temperature extremes projected...

Recently amplified arctic warming has contributed to a continual global warming trend

The existence and magnitude of the recently suggested global warming hiatus, or slowdown, have been strongly debated1–3. Although various physical processes4–8 have been examined to elucidate this phenomenon, the accuracy and completeness of observational data that comprise global average surface air temperature (SAT) datasets is a concern9,10. In particular, these datasets lack either complete geographic coverage or in situ observations over the Arctic, owing to the sparse observational network in this area9. As a consequence, the contribution of Arctic warming to global SAT changes may have been underestimated, leading to an uncertainty in the hiatus debate. Here, we constructed a new Arctic SAT dataset using the most recently updated global SATs2 and a drifting buoys based Arctic SAT dataset11 through employing the ‘data interpolating empirical orthogonal functions’ method12. Our estimate of global SAT rate of increase is around 0.112 °C per decade, instead of 0.05 °C per decade from IPCC AR51, for 1998–2012. Analysis of this dataset shows that the amplified Arctic warming over the past decade has significantly contributed to a continual global warming trend, rather than a hiatus or slowdown.The Arctic is under-represented in surface temperature datasets and this could affect estimates of global warming. A new dataset with greater coverage of the Arctic shows a higher warming rate of 0.112 °C per decade compared to 0.005 °C from IPCC AR5.

The Extratropical Northern Hemisphere Temperature Reconstruction during the Last Millennium Based on a Novel Method

Large-scale climate history of the past millennium reconstructed solely from tree-ring data is prone to underestimate the amplitude of low-frequency variability. In this paper, we aimed at solving this problem by utilizing a novel method termed “MDVM”, which was a combination of the ensemble empirical mode decomposition (EEMD) and variance matching techniques. We compiled a set of 211 tree-ring records from the extratropical Northern Hemisphere (30–90°N) in an effort to develop a new reconstruction of the annual mean temperature by the MDVM method. Among these dataset, a number of 126 records were screened out to reconstruct temperature variability longer than decadal scale for the period 850–2000 AD. The MDVM reconstruction depicted significant low-frequency variability in the past millennium with evident Medieval Warm Period (MWP) over the interval 950–1150 AD and pronounced Little Ice Age (LIA) cumulating in 1450–1850 AD. In the context of 1150-year reconstruction, the accelerating warming in 20th century was likely unprecedented, and the coldest decades appeared in the 1640s, 1600s and 1580s, whereas the warmest decades occurred in the 1990s, 1940s and 1930s. Additionally, the MDVM reconstruction covaried broadly with changes in natural radiative forcing, and especially showed distinct footprints of multiple volcanic eruptions in the last millennium. Comparisons of our results with previous reconstructions and model simulations showed the efficiency of the MDVM method on capturing low-frequency variability, particularly much colder signals of the LIA relative to the reference period. Our results demonstrated that the MDVM method has advantages in studying large-scale and low-frequency climate signals using pure tree-ring data.

Changes in Extremely Hot Summers over the Global Land Area under Various Warming Targets

Summer temperature extremes over the global land area were investigated by comparing 26 models of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) with observations from the Goddard Institute for Space Studies (GISS) and the Climate Research Unit (CRU). Monthly data of the observations and models were averaged for each season, and statistics were calculated for individual models before averaging them to obtain ensemble means. The summers with temperature anomalies (relative to 1951–1980) exceeding 3σ (σ is based on the local internal variability) are defined as “extremely hot”. The models well reproduced the statistical characteristics evolution, and partly captured the spatial distributions of historical summer temperature extremes. If the global mean temperature increases 2°C relative to the pre-industrial level, “extremely hot” summers are projected to occur over nearly 40% of the land area (multi-model ensemble mean projection). Summers that exceed 5σ warming are projected to occur over approximately 10% of the global land area, which were rarely observed during the reference period. Scenarios reaching warming levels of 3°C to 5°C were also analyzed. After exceeding the 5°C warming target, “extremely hot” summers are projected to occur throughout the entire global land area, and summers that exceed 5σ warming would become common over 70% of the land area. In addition, the areas affected by “extremely hot” summers are expected to rapidly expand by more than 25%/°C as the global mean temperature increases by up to 3°C before slowing to less than 16%/°C as the temperature continues to increase by more than 3°C. The area that experiences summers with warming of 5σ or more above the warming target of 2°C is likely to maintain rapid expansion of greater than 17%/°C. To reduce the impacts and damage from severely hot summers, the global mean temperature increase should remain low.

Causes of model dry and warm bias over central U.S. and impact on climate projections

Climate models show a conspicuous summer warm and dry bias over the central United States. Using results from 19 climate models in the Coupled Model Intercomparison Project Phase 5 (CMIP5), we report a persistent dependence of warm bias on dry bias with the precipitation deficit leading the warm bias over this region. The precipitation deficit is associated with the widespread failure of models in capturing strong rainfall events in summer over the central U.S. A robust linear relationship between the projected warming and the present-day warm bias enables us to empirically correct future temperature projections. By the end of the 21st century under the RCP8.5 scenario, the corrections substantially narrow the intermodel spread of the projections and reduce the projected temperature by 2.5 K, resulting mainly from the removal of the warm bias. Instead of a sharp decrease, after this correction the projected precipitation is nearly neutral for all scenarios.Climate models repeatedly show a warm and dry bias over the central United States, but the origin of this bias remains unclear. Here the authors associate this bias to precipitation deficits in models and after applying a correction, projected precipitation in this region shows no significant changes.

A Strategy for Merging Objective Estimates of Global Daily Precipitation from Gauge Observations, Satellite Estimates, and Numerical Predictions

This paper describes a strategy for merging daily precipitation information from gauge observations, satellite estimates (SEs), and numerical predictions at the global scale. The strategy is designed to remove systemic bias and random error from each individual daily precipitation source to produce a better gridded global daily precipitation product through three steps. First, a cumulative distribution function matching procedure is performed to remove systemic bias over gauge-located land areas. Then, the overall biases in SEs and model predictions (MPs) over ocean areas are corrected using a rescaled strategy based on monthly precipitation. Third, an optimal interpolation (OI)–based merging scheme (referred as the HL-OI scheme) is used to combine unbiased gauge observations, SEs, and MPs to reduce random error from each source and to produce a gauge—satellite–model merged daily precipitation analysis, called BMEP-d (Beijing Climate Center Merged Estimation of Precipitation with daily resolution), with complete global coverage. The BMEP-d data from a four-year period (2011–14) demonstrate the ability of the merging strategy to provide global daily precipitation of substantially improved quality. Benefiting from the advantages of the HL-OI scheme for quantitative error estimates, the better source data can obtain more weights during the merging processes. The BMEP-d data exhibit higher consistency with satellite and gauge source data at middle and low latitudes, and with model source data at high latitudes. Overall, independent validations against GPCP-1DD (GPCP one-degree daily) show that the consistencies between BMEP-d and GPCP-1DD are higher than those of each source dataset in terms of spatial pattern, temporal variability, probability distribution, and statistical precipitation events.

Narrowing the spread in CMIP5 model projections of air-sea CO2 fluxes.

Large spread appears in the projection of air-sea CO2 fluxes using the latest simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Here, two methods are applied to narrow this spread in 13 CMIP5 models. One method involves model selection based on the ability of models to reproduce the observed air-sea CO2 fluxes from 1980 to 2005. The other method involves constrained estimation based on the strong relationship between the historical and future air-sea CO2 fluxes. The estimated spread of the projected air-sea CO2 fluxes is effectively reduced by using these two approaches. These two approaches also show great agreement in the global ocean and three regional oceans of the equatorial Pacific Ocean, the North Atlantic Ocean and the Southern Ocean, including the average state and evolution characteristics. Based on the projections of the two approaches, the global ocean carbon uptake will increase in the first half of the 21st century then remain relatively stable and is projected to be 3.68–4.57 PgC/yr at the end of 21st century. The projections indicate that the increase in the CO2 uptake by the oceans will cease at the year of approximately 2070.

Publisher Correction: Causes of model dry and warm bias over central U.S. and impact on climate projections

The original version of this Article contained an error in Figure 2. In panel a, the x axis of the graph was incorrectly labeled ‘precipitation bias’, and should have read ‘negative precipitation bias’. This error has been corrected in both the PDF and HTML versions of the Article.

Publisher Correction: Recently amplified arctic warming has contributed to a continual global warming trend

In the version of this Letter originally published, the increments on the y axis of Fig. 3 were incorrectly labelled as ‘0.0; 0.2; 0.2; 0.3’; they should have read ‘0.0; 0.1; 0.2; 0.3’. This has now been corrected in all versions of the Letter.

Erratum: Corrigendum: Narrowing the spread in CMIP5 model projections of air-sea CO2 fluxes

Scientific Reports 6: Article number: 37548; published online: 28 November 2016; updated: 10 April 2017. The Acknowledgements section in this Article is incomplete. “We acknowledge the World Climate Research Programme’s Working Group on Coupled Modeling, which is responsible for CMIP. We thank the climate groups (listed in Table 1) for providing the model output.