# Near-Term Projections of Global and Regional Land Mean Temperature Changes Considering Both the Secular Trend and Multidecadal Variability

• Corresponding author: Cheng QIAN, qianch@tea.ac.cn
• Funds:

Supported by the National Key Research and Development Program of China (2016YFA0600404), Youth Innovation Promotion Association of the Chinese Academy of Sciences (2016075), and Jiangsu Collaborative Innovation Center for Climate Change

• doi: 10.1007/s13351-018-7136-4
• Near-term climate projections are needed by policymakers; however, these projections are difficult because internally generated climate variations need to be considered. In this study, temperature change scenarios in the near-term period 2017–35 are projected at global and regional scales based on a refined multi-model ensemble approach that considers both the secular trend (ST) and multidecadal variability (MDV) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations. The ST and MDV components are adaptively extracted from each model simulation by using the ensemble empirical mode decomposition (EEMD) filter, reconstructed via the Bayesian model averaging (BMA) method for the historical period 1901–2005, and validated for 2006–16. In the simulations of the " medium” representative concentration pathways scenario during 2017–35, the MDV-modulated temperature change projected via the refined approach displays an increase of 0.44°C (90% uncertainty range from 0.30 to 0.58°C) for global land, 0.48°C (90% uncertainty range from 0.29 to 0.67°C) for the Northern Hemispheric land (NL), and 0.29°C (90% uncertainty range from 0.23 to 0.35°C) for the Southern Hemispheric land (SL). These increases are smaller than those projected by the conventional arithmetic mean approach. The MDV enhances the ST in 13 of 21 regions across the world. The largest MDV-modulated warming effect (46%) exists in central America. In contrast, the MDV counteracts the ST in NL, SL, and eight other regions, with the largest cooling effect (220%) in Alaska.
• Fig. 1.  A diagram showing the 21 regions analyzed in this study. The regions are presented by colored boxes according to continent: Australia (red), South America (green), North America (blue), Africa (magenta), Europe (purple), and Asia (black). Giorgi and Francisco (2000) provided the definitions of the regions with latitude and longi-tude information.

Fig. 2.  The time series of the global mean land surface air temperature (TAS) anomalies (black line, relative to the 1961–90 base period) and the ST (green line) and MDV (blue line) components, together with their combination (ST + MDV), during the period 1901–2005.

Fig. 3.  The (a) MDV, (b) ST, and (c) ST + MDV in the global mean land surface air temperature (TAS) anomaly series from 1901 to 2005, compared between the observations (solid black line) and the multi-model ensemble results based on the AMME-BMA approach (solid red line) and the MME approach (solid blue line). The dotted lines are the corresponding components during the validation period 2006–16 and the future period 2017–35. The shaded part denotes the 5th–95th percentile range of the model simulations of MDV, ST, and ST + MDV.

Fig. 4.  (a) Box plots of the BI for MDV (1901–2005) in the GL, NL, and SL regions. Each box represents the interquartile range (IQR) and contains 50% of the results of 34 CMIP5 models for a region: the upper edge of the box represents the 75th percentile [upper quartile (UQ)], while the lower edge is the 25th percentile [lower quartile (LQ)]. The horizontal black line within the box is the median. The “+” signs represent outliers [either > (UQ + 1.5 × IQR) or < (LQ – 1.5 × IQR)]. The vertical dashed lines indicate the range of the non-outliers. The blue squares represent the BIs of the MME-based results, and the red circles represent those of the AMME-BMA results for the historical period. (b) and (c) are the same as (a), but for ST and ST + MDV, respectively.

Fig. 5.  (a, c, e) are the same as in Fig. 3, but for the Northern Hemispheric land; (b, d, f) are also the same as in Fig. 3, but for the Southern Hemispheric land.

Fig. 6.  (a) The MDV in the Giorgi–Francisco regions for 1901–2005 and 2006–35 under RCP4.5. The red curves indicate the AMME-BMA results and the blue curves indicate the MME results. The thin solid black lines show the observed MDV based on the HadCRU4 data. The gray shading shows the 5th–95th percentile range of the multi-model simulations. (b) and (c) are the same as (a), but for ST and ST + MDV, respectively. The axes are colored according to the continents, which are defined in the caption of Fig. 1.

Fig. 7.  Box plots of warming increments (ΔT) due to (a) ST and (b) ST + MDV from 2017 to 2035. The blue lines represent the MME-based results and red lines represent the AMME-BMA results.

Fig. 8.  The ratio (R) of the warming increment induced by the MDV to that induced by the sum of MDV and ST for the GL, NL, SL, and the 21 Giorgi–Francisco regions during the near-term period (2017–35).

###### 通讯作者: 陈斌, bchen63@163.com
• 1.

沈阳化工大学材料科学与工程学院 沈阳 110142

## Near-Term Projections of Global and Regional Land Mean Temperature Changes Considering Both the Secular Trend and Multidecadal Variability

###### Corresponding author: Cheng QIAN, qianch@tea.ac.cn;
• 1. CAS Key Laboratory of Regional Climate–Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences (CAS), Beijing 100029
• 2. Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089
• 3. University of Chinese Academy of Sciences, Beijing 100049
• 4. Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing 100081
• 5. Joint Center for Global Change Studies, Beijing Normal University, Beijing 100875
Funds: Supported by the National Key Research and Development Program of China (2016YFA0600404), Youth Innovation Promotion Association of the Chinese Academy of Sciences (2016075), and Jiangsu Collaborative Innovation Center for Climate Change

Abstract: Near-term climate projections are needed by policymakers; however, these projections are difficult because internally generated climate variations need to be considered. In this study, temperature change scenarios in the near-term period 2017–35 are projected at global and regional scales based on a refined multi-model ensemble approach that considers both the secular trend (ST) and multidecadal variability (MDV) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations. The ST and MDV components are adaptively extracted from each model simulation by using the ensemble empirical mode decomposition (EEMD) filter, reconstructed via the Bayesian model averaging (BMA) method for the historical period 1901–2005, and validated for 2006–16. In the simulations of the " medium” representative concentration pathways scenario during 2017–35, the MDV-modulated temperature change projected via the refined approach displays an increase of 0.44°C (90% uncertainty range from 0.30 to 0.58°C) for global land, 0.48°C (90% uncertainty range from 0.29 to 0.67°C) for the Northern Hemispheric land (NL), and 0.29°C (90% uncertainty range from 0.23 to 0.35°C) for the Southern Hemispheric land (SL). These increases are smaller than those projected by the conventional arithmetic mean approach. The MDV enhances the ST in 13 of 21 regions across the world. The largest MDV-modulated warming effect (46%) exists in central America. In contrast, the MDV counteracts the ST in NL, SL, and eight other regions, with the largest cooling effect (220%) in Alaska.

Reference (66)

/