Surface Air Temperature Trends Research Articles

Estimation of Reference Crop Evapotranspiration in Northwest China

Based on the daily meteorological data from 1956 to 2011 in Northwest (NW) China and the Penman-Monteith (PM) equation, the regional reference crop evapotranspiration (ET0) is estimated. The ET0 variations in time series and spatial distributions are analyzed. The trend analysis, Mann-Kendall (M-K) test, wavelet analysis, stepwise regression and EOF analysis methods are used to investigate the spatiotemporal variability of ET0 and its contributing climatic factors, the mutation of ET0, the period of ET0, and the main influencing meteorological factors, respectively. Major conclusions are obtained as follows: (1) In the past 56 years, the trend of average annual ET0 time series in the NW China is significantly reduced, the differences exists in different seasons, i.e., the trends of ET0 in spring (-0.26mm/a), summer (-0.72mm/a) and autumn (-0.31mm/a) are decreased, respectively, the ET0 in winter is slowly increased (0.02mm/a). (2) The region which ET0 decreased most is located at the field from Kumul to Hotan (from northeast to southwest). ET0 has a sharply decrease around the 1980s, with a multiple-time scale nesting complex structure in the period. The first, second and third EOF models account 36.84%, 13.87% and 9.04% for the explained variance, respectively. The summer EOF model is the main contributor to the annual first model. (3) The upward trend of mean surface air temperature (T) and the decreased trend of sunshine duration (SD), relative humidity (RH) and wind speed at 2 m high (U2) induce ET0 to decline. The variability of annual ET0 rate is most influenced by the variations of U2, followed by SD, RH and T, which is influenced by various climatic variables. The investigation of spatiotemporal variability of ET0 and its contributor meteorological factors may help us better understand how ET0 responds to regional climate change.

Contribution of SST change to multidecadal global and continental surface air temperature trends between 1910 and 2013

The global mean surface air temperature (GMST) shows multidecadal variability over the period of 1910–2013, with an increasing trend. This study quantifies the contribution of hemispheric surface air temperature (SAT) variations and individual ocean sea surface temperature (SST) changes to the GMST multidecadal variability for 1910–2013. At the hemispheric scale, both the Goddard Institute for Space Studies (GISS) observations and the Community Earth System Model (CESM) Community Atmosphere Model 5.3 (CAM5.3) simulation indicate that the Northern Hemisphere (NH) favors the GMST multidecadal trend during periods of accelerated warming (1910–1945, 1975–1998) and cooling (1940–1975, 2001–2013), whereas the Southern Hemisphere (SH) slows the intensity of both warming and cooling processes. The contribution of the NH SAT variation to the GMST multidecadal trend is higher than that of the SH. We conduct six experiments with different ocean SST forcing, and find that all the oceans make positive contributions to the GMST multidecadal trend during rapid warming periods. However, only the Indian, North Atlantic, and western Pacific oceans make positive contributions to the GMST multidecadal trend between 1940 and 1975, whereas only the tropical Pacific and the North Pacific SSTs contribute to the GMST multidecadal trend between 2001 and 2013. The North Atlantic and western Pacific oceans have important impacts on modulating the GMST multidecadal trend across the entire 20th century. Each ocean makes different contributions to the SAT multidecadal trend of different continents during different periods.

Comparison of North Atlantic Oscillation‐related changes in the North Atlantic sea ice and associated surface quantities on different time scales

AbstractBy separating variations on different time scales, the present study reveals important differences in the North Atlantic Oscillation (NAO)‐related sea ice concentration (SIC), surface air temperature (SAT), and sea surface temperature (SST) patterns for trend, interdecadal, and interannual variations. The SIC has a prominent decreasing trend in the Greenland Sea and the Barents Sea, collocating with an increasing SAT trend and a weak increasing SST trend in the high‐latitude North Atlantic. The wind trends display a weak NAO signal. Corresponding to the positive interdecadal NAO phase, the SIC shows a decreasing trend in the Greenland and Barents Seas. The SAT change features a west negative‐east positive pattern along with positive anomalies extending to the Greenland Sea. The SST change is very weak in the Greenland Sea. Corresponding to the positive interannual NAO phase, the SIC change is opposite between the Greenland/Barents Seas and the Labrador Sea. The SAT change is characterized by a broad west–east pattern over the mid‐high latitudes. The SST change features an east–west dipole pattern in the mid‐latitude North Atlantic Ocean. In both interdecadal and interannual variations, NAO‐related meridional wind anomalies induce anomalous advection that contributes to the SAT change together with upward long‐wave radiation. The SIC and SAT changes are coupled closely through surface heat fluxes in all the three time scales. The present results suggest that it is necessary to distinguish time scales in studying the relationship among SIC, SAT, and SST variations.

Attribution of the East Asian Winter Temperature Trends During 1979–2018: Role of External Forcing and Internal Variability

AbstractAttributing the surface air temperature (SAT) trends at regional scale to internally generated and externally forced components is a major challenge. Based on the observations and ensemble simulations of climate models, we apply a “dynamical adjustment” methodology to estimate quantitatively the contribution of the internal and forced dynamic and thermodynamic components to the East Asian winter SAT trends during 1979–2018. The observed winter SAT trends are strongly influenced by internal variability, especially in the northern East Asia (NEA). The internal component of observed SAT trends is largely dynamically induced, whereas the forced component is controlled by thermodynamics. The internally generated variability offsets the forced warming by more than 70% in the NEA, leading to a weak observed warming in the NEA. In contrast, internal contributions are small in the southern East Asia. The internally generated SAT changes in the NEA are closely tied to the multidecadal changes of Arctic Oscillation.

A Significant Bias of Tmax and Tmin Average Temperature and Its Trend

AbstractThe systematic bias of the estimated average temperature using dailyTmaxandTminrecords relative to the standard average temperature of four time-equidistant observations and its effect on the estimated trend of long-term temperature change have not been well understood. This paper attempts to evaluate the systematic bias across mainland China using the daily data of national observational stations. The results revealed that the positive bias of annual mean temperature was large, reaching 0.58°C nationally on average; regional average bias was lowest in the northwest arid region and highest in the Qinghai–Tibetan Plateau; the bias was low in spring and summer and high in autumn and winter, reaching its lowest point in mid- and late May and highest point in early November. Furthermore, the bias showed a significant upward trend in the past 50 years, with a rising rate of 0.021°C (10 yr)−1, accounting for about 12% of the overall warming as estimated from the data of the observational network; the largest positive trend bias was found in the northwest arid region, while the east monsoon region experienced the smallest change; the most remarkable increase of the bias occurred after early 1990s. These results indicate that the customarily applied method to calculate daily and monthly mean temperature usingTmaxandTminsignificantly overestimates the climatological mean and the long-term trend of surface air temperature in mainland China.

Climatic Reasons for the Current Expansion of the Range of the Italian Locust in Russia and Neighboring Countries

The species climatic range of a dangerous locust pest, called the Italian locust, was modelled for the territories of Russia and neighboring countries over two time periods of 1956–1975 and 1996–2015. On the basis of analysis of the constructed schematic map, the northward expansion of the climatic range of the Italian locust in 1996–2015 in comparison to the period of 1956–1975 is shown. The expansion is explained by the increasing trend of the average global surface air temperature in recent decades. The obtained data can be used for monitoring locust pests in agriculture.

Future projection of summer surface air temperature trend over central India: Role of external forcing and internal variability

AbstractThis study investigates the relative contribution of internal variability and external forcings on summer (June–August) surface air temperature (SAT) over the central Indian landmass. Here we use Community Earth System Model Large Ensemble (CESM‐LE) data to assess the historical (1966–2005) and future (2010–2060) climate change in presence of internal climate variability. The summer SAT trend during the historical period exhibits an amplified cooling (<−3°C), whereas a warming trend (≥4°C) is projected in all the ensemble members under RCP8.5 scenario. The total trend is then partitioned into contributions from the externally forced response and internal climatic variability. Over the Indian region, the external forcing displays a strong cooling trend during the historical period and warming trend under RCP8.5 scenario. On the other hand, natural variability displays mainly cooling trends and it introduces a wide range of uncertainty to the future projection in climate models. In historical period, the signal‐to‐noise ratio (SNR), that is, ratio of external forcings and internal variability, is less than 1, which indicates that the internal climatic variability dominates over the forced response. But in future decades the SNR is much higher than 1, that is, external forcing overrides the internal variability. However, to a greater extent natural variability will mask the warming trend over the Indian region, even under RCP8.5 scenario.

Climatological Changes: Meteorological Parameters Affecting the Spatial Redistribution of U.S. Tornadoes

Climatological changes in the environments of key meteorological parameters that affect Significant Tornado Days (SigTorDs) have been determined for two active tornado regions defined as Box α and Box β, centered, respectively, over Oklahoma and Alabama and their respective environs. The North American Regional Reanalysis data was selected for 1980–2013, providing two successive 17-year periods corresponding to the last 34 years of previous research findings that focused on the aforementioned regions. This data record also corresponds to an increasing surface air temperature trend for the continental United States. Period I (1980–1996) and Period II (1997–2013) defined the years of changing environments for the two regions studied. Environmental parameters investigated and compared to SigTorDs were surface-based convective available potential energy (CAPE), storm relative helicity (0–3 km), bulk wind difference (1,000 mb to 500 mb), and lifted condensation level (LCL). Environments have changed to fewer SigTorDs (130 to 74) in Box α from Period I to Period II, dominated by decreasing frequency of storm relative helicity and slightly increasing CAPE. Box β is characterized by more SigTorDs (94 to 119) in Period II with an environment dominated by increasing frequency of storm relative helicity and minimal change in CAPE. These results support the importance of the changes in storm relative helicity (as opposed to CAPE) in explaining the eastward shift in U.S. tornado activity.

Projections of climate changes over mid-high latitudes of Eurasia during boreal spring: uncertainty due to internal variability

This study examines uncertainties of projected spring surface air temperature (SAT) and precipitation trends during 2006–2060 at regional scales over the mid-high latitudes of Eurasia due to internal variability based on 40 ensemble members projections of CCSM3. The 40 ensemble members are initiated at a slightly different atmospheric conditions but with the same external forcing. Thus, the differences of the projected spring SAT and precipitation trends among the 40 ensemble members are attributed to the internal variability. Results suggest that superposition of internal variability and external forcing leads to a large spread of projected spring SAT and precipitation trends over Eurasia. In comparison, the projected spring precipitation trend has a larger uncertainty than the spring SAT trend. In particular, the signal-to-noise ratios of spring SAT (precipitation) trends are larger than two (one) over most regions of the mid-high latitudes of Eurasia. The internal atmospheric circulation variability is an important source of the uncertainties of the projected spring SAT and precipitation trends. The first mode of the internal atmospheric circulation variability resembles the Arctic Oscillation pattern. The second mode displays feature similar to the North Atlantic Oscillation and anomalous Siberian High patterns. A dynamical adjustment technique is employed to reduce internal atmospheric circulation generated variability in spring SAT and precipitation trends. Result indicates that projected trends of the dynamically adjusted spring SAT and precipitation over the next 55 years over Eurasia are similar across the 40 ensemble members both in the spatial structure and amplitude.

The Role of the Tropically Excited Arctic Warming Mechanism on the Warm Arctic Cold Continent Surface Air Temperature Trend Pattern

AbstractThe December–February surface air temperature (SAT) trend is examined for all consecutive 20‐year time periods between 1979 and 2017, from which a transition from a cold‐Arctic‐warm‐continent toward a warm‐Arctic‐cold‐continent trend pattern is evident. This transition is accompanied by a consistent transition in the sea level pressure trend pattern that supports warm air advection over the Arctic and cold air advection over the continents. The regression of the detrended December–February‐average SAT onto a detrended index defined to quantify the east‐west gradient of tropical Pacific sea surface temperature is characterized by a warm‐Arctic‐cold‐continent pattern much like the SAT trend pattern observed in recent decades. A decadal timescale warming of the western tropical Pacific water has increased the east‐west gradient of tropical Pacific sea surface temperature, thus contributing to the observed extratropical SAT trend transition.

Evaluation of CMIP5 ability to reproduce twentieth century regional trends in surface air temperature and precipitation over CONUS

The ability of the 5th phase of the Coupled Model Intercomparison Project (CMIP5) to reproduce twentieth-century climate trends over the seven CONUS regions of the National Climate Assessment is evaluated. This evaluation is carried out for summer and winter for three time periods, 1895–1939, 1940–1979, and 1980–2005. The evaluation includes all 206 CMIP5 historical simulations from 48 unique models and their multi-model ensemble (MME), as well as a gridded in situ dataset of surface air temperature and precipitation. Analysis is performed on both individual members and the MME, and considers reproducing the correct sign of the trends by the members as well as reproducing the trend values. While the MME exhibits some trend bias in most cases, it reproduces historical temperature trends with reasonable fidelity for summer for all time periods and all regions, including at the CONUS scale, except the Northern Great Plains from 1895 to 1939 and Southeast during 1980–2005. Likewise, for DJF, the MME reproduces historical temperature trends across all time periods over all regions, including at the CONUS scale, except the Southeast from 1895 to 1939 and the Midwest during 1940–1979. Model skill was highest across all of the seven regions during JJA and DJF for the 1980–2005 period. The quantitatively best result is seen during DJF in the Southwest region with at least 74% of the ensemble members correctly reproducing the observed trend across all of the time periods. No clear trends in MME precipitation were identified at these scales due to high model precipitation variability.

Strong Wind Speed Events over Antarctica and Its Surrounding Oceans

AbstractStrong wind events (SWEs) over Antarctica and its surrounding oceans are investigated using gridded surface wind data from the ERA-Interim for the 1979–2017 period. Throughout the year, SWEs are more prevalent over the coastal region of East Antarctica where mean surface wind speeds are also higher. The occurrences of SWEs appear to be accompanied by positive anomalies in surface temperature and negative (positive) anomalies in mean sea level pressure related to cyclone (anticyclone) activity over the Ronne and Ross Ice Shelves and coastal regions (the inland areas of East Antarctica). The interannual variability of the SWE occurrences appears to be related to the southern annular mode (SAM) and, to a lesser degree, ENSO. The trends of SWE in the recent four decades exhibit considerable regional variations that are consistent with the trends in seasonal mean wind speed and surface air temperature, and can be largely explained by the variations in the sea level pressure trends across the region.

Uncertainty in near-term global surface warming linked to tropical Pacific climate variability

Climate models generally simulate a long-term slowdown of the Pacific Walker Circulation in a warming world. However, despite increasing greenhouse forcing, there was an unprecedented intensification of the Pacific Trade Winds during 1992–2011, that co-occurred with a temporary slowdown in global surface warming. Using ensemble simulations from three different climate models starting from different initial conditions, we find a large spread in projected 20-year globally averaged surface air temperature trends that can be linked to differences in Pacific climate variability. This implies diminished predictive skill for global surface air temperature trends over decadal timescales, to a large extent due to intrinsic Pacific Ocean variability. We show, however, that this uncertainty can be considerably reduced when the initial oceanic state is known and well represented in the model. In this case, the spatial patterns of 20-year surface air temperature trends depend largely on the initial state of the Pacific Ocean.

Global Heat Wave Hazard Considering Humidity Effects during the 21st Century.

Humidity is a significant factor contributing to heat stress, but without enough consideration in studies of quantifying heat hazard or heat risk assessment. Here, the simplified wet-bulb globe temperature (WBGT) considering joint effects of temperature and humidity was utilized as a heat index and the number of annual total heat wave days (HWDs) was employed to quantify heat hazard. In order to evaluate the humidity effects on heat waves, we quantified the difference in the number of HWDs over global land based on air temperature and WBGT. Spatial and temporal changes in surface air temperature, relative humidity, WBGT, and the difference in HWDs were analyzed using multi-model simulations for the reference period (1986–2005) and different greenhouse gas emission scenarios. Our analysis suggests that annual mean WBGT has been increasing since 1986, which is consistent with the rising trend in surface air temperature despite a slight decrease in relative humidity. Additionally, changes in annual mean WBGT are smaller and more spatially uniform than those in annual mean air temperature as a cancelation effect between temperature and water vapor. Results show that there is an underestimation of around 40–140 days in the number of HWDs per year in most regions within 15° latitude of the equator (the humid and warm tropics) during 2076–2095 without considering humidity effects. However, the estimation of HWDs has limited distinction between using WBGT and temperature alone in arid or cold regions.

Towards normal Siberian winter temperatures?

AbstractSiberia is a region where despite global warming a winter cooling trend has been observed over last decades. This cooling trend and its potential linkage to Arctic sea ice loss are controversially discussed. However, recent winters have not been taken into account so far. Here, we analyse ERA‐Interim reanalysis data until 2017 and ERA20C reanalysis to investigate the robustness of the winter surface air temperature trends to updated and extended time periods. Our results show that winter temperatures in Siberia were above normal after 2013 leading to strongly reduced cooling trends since 1980. The trend before 2014 was dominated by four cold winters between 2010 and 2013. These cold winters were mainly caused by strong negative phases of the North Atlantic Oscillation (NAO), except for the winter 2011/2012, where the NAO was positive and a strongly negative phase of the Pacific Decadal Oscillation (PDO) in combination with low sea ice in the Barents Sea caused the cold winter. Both NAO and PDO shift from more negative to positive phases in 2014 and contribute to a return to warmer Siberian temperatures. Furthermore, the NAO shows no trend between 1980 and 2017 indicating that the suggested linkage between Arctic sea ice loss and a negative trend in this mode is not robust. However, continuously low Arctic sea ice in recent years and a slightly negative trend in the PDO since 1980 contribute to the remaining observed cold trends over parts of Eurasia between 1980 and 2017.

Recent Surface Air Temperature Change over Mainland China Based on an Urbanization-Bias Adjusted Dataset

AbstractA dataset from 763 national Reference Climate and Basic Meteorological Stations (RCBMS) was used to analyze surface air temperature (SAT) change in mainland China. The monthly historical observational records had been adjusted for urbanization bias existing in the data series of size-varied urban stations, after they were corrected for data inhomogeneities mainly caused by relocation and instrumentation. The standard procedures for creating area-averaged temperature time series and for calculating linear trend were used. Analyses were made for annual and seasonal mean temperature. Annual mean SAT in mainland China as a whole rose by 1.24°C for the last 55 years, with a warming rate of 0.23°C decade−1. This was close to the warming of 1.09°C observed in global mean land SAT over the period 1951–2010. Compared to the SAT before correction, after-corrected data showed that the urbanization bias had caused an overestimate of the annual warming rate of more than 19.6% during 1961–2015. The winter, autumn, spring, and summer mean warming rates were 0.28°, 0.23°, 0.23°, and 0.15°C decade−1, respectively. The spatial patterns of the annual and seasonal mean SAT trends also exhibited an obvious difference from those of the previous analyses. The largest contrast was a weak warming area appearing in central parts of mainland China, which included a small part of southwestern North China, the northwestern Yangtze River, and the eastern part of Southwest China. The annual mean warming trends in Northeast and North China obviously decreased compared to the previous analyses, which caused a relatively more significant cooling in Northeast China after 1998 under the background of global warming slowdown.

The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6

Abstract. The main advancements of the Beijing Climate Center (BCC) climate system model from phase 5 of the Coupled Model Intercomparison Project (CMIP5) to phase 6 (CMIP6) are presented, in terms of physical parameterizations and model performance. BCC-CSM1.1 and BCC-CSM1.1m are the two models involved in CMIP5, whereas BCC-CSM2-MR, BCC-CSM2-HR, and BCC-ESM1.0 are the three models configured for CMIP6. Historical simulations from 1851 to 2014 from BCC-CSM2-MR (CMIP6) and from 1851 to 2005 from BCC-CSM1.1m (CMIP5) are used for models assessment. The evaluation matrices include the following: (a) the energy budget at top-of-atmosphere; (b) surface air temperature, precipitation, and atmospheric circulation for the global and East Asia regions; (c) the sea surface temperature (SST) in the tropical Pacific; (d) sea-ice extent and thickness and Atlantic Meridional Overturning Circulation (AMOC); and (e) climate variations at different timescales, such as the global warming trend in the 20th century, the stratospheric quasi-biennial oscillation (QBO), the Madden–Julian Oscillation (MJO), and the diurnal cycle of precipitation. Compared with BCC-CSM1.1m, BCC-CSM2-MR shows significant improvements in many aspects including the tropospheric air temperature and circulation at global and regional scales in East Asia and climate variability at different timescales, such as the QBO, the MJO, the diurnal cycle of precipitation, interannual variations of SST in the equatorial Pacific, and the long-term trend of surface air temperature.

Backward and forward drift trajectories of sea ice in the northwestern Arctic Ocean in response to changing atmospheric circulation

AbstractTo track sea ice motion, four ice‐tethered buoys were deployed at 84.6°N and 144.3°W, 87.3°N and 172.3°W, 81.1°N and 157.4°W, and 82.8°N and 166.5°W in summers of 2008, 2010, 2014, and 2016, respectively. In addition, the remote sensed ice motion product provided by National Snow and Ice Data Center was used to reconstruct backward and forward ice drifting trajectories from the buoy deployment sites during 1979–2016. Sea ice in the central Arctic Ocean in late summer is trending to have travelled from lower latitudes, and to be advected to the region more involved in the Transpolar Drift Stream (TDS) during 1979–2016. The strengthened TDS has played a crucial role in Arctic sea ice loss from a dynamic perspective. The trajectory of ice is found to be significantly related to atmosphere circulation indices. The Central Arctic Index (CAI), defined as the difference in sea level pressure between 84°N, 90°W and 84°N, 90°E, can explain 34–40% of the meridional displacement along the backward trajectories, and it can explain 27–40% of the zonal displacement along the forward trajectories. The winter Beaufort High (BH) anomaly can explain 18–27% of the zonal displacement. Under high positive CAI values or high negative winter BH anomalies, floes from the central Arctic tended to be advected out of the Arctic Ocean through Fram Strait or other marginal gateways. Conversely, under high negative CAI values or high positive winter BH anomalies, ice tended to become trapped within a region close to the North Pole or it drifted into the Beaufort Gyre region. The long‐term trend and spatial change in Arctic surface air temperature were more remarkable during the freezing season than the melt season because most energy from the lower troposphere is used to melt sea ice and warm the upper ocean during summer.

Long-term change in surface air temperature over DPR Korea, 1918–2015

Land surface air temperature (SAT) change is one of the core issues in monitoring and assessing regional climate change. In this study, the characteristics of SAT change over DPR Korea for the period 1918–2015 were investigated using a high-quality historical dataset. Results show that the region-averaged annual mean SAT increased 0.21 °C/decade for the period 1918–2015 on the basis of data from four stations and 0.19 °C/decade for the period 1941–2015 as estimated based on data from nine stations. Before the 1970s, Pyongyang station in the central region experienced the largest warming trend. Linear trends of seasonal mean SAT during 1941–1970 were negative for all seasons in eastern coast and for summer and autumn in western coast and northern inland areas. Since 1971, however, the annual and seasonal mean SAT trends have shifted to positive values in all regions, with winter experiencing the most rapid warming. During the period of global warming slowdown since 1998 or 2000, no significant seasonal warming trend of wintertime was detectable, and this caused the smallest winter warming for the last 45 years. Other seasons also witnessed a generally weakened warming during 1971–2015 compared to that of 1971–2000. The results of the study will help in understanding regional climate change and in assessing the impacts of climate change on economic and natural ecosystems in the country.

Disentangling the influencing factors driving the cooling trend in boreal summer over Indo-Gangetic river basin, India: role of Atlantic multidecadal oscillation (AMO)

Using the Community Earth System Model (CESM)-Large Ensemble (LE) surface air temperature (SAT) data, we investigate the multidecadal changes in SAT variability over Central Indian landmass, particularly the Indo-Gangetic (IG) river basin. This region comes under the active influence of the Indian summer monsoon, and during the summer monsoon months (JJA), we observe an amplified cooling (< − 3 °C) trend (1961–2000) in SAT. This SAT trend is considered as a superposition of external forcings and natural climatic variability. The forced response is computed by averaging the trend in 35 ensemble members, which displays a moderate cooling trend due to aerosol-, ozone-, and volcano-only forcings. But the internal variability introduces a wide range of uncertainties in SAT, with majority of the members display a strong cooling trend in the Central Indian region. During the entire period, natural climatic variability dominates over the forced response, which strongly overrides the greenhouse gas (GHG) warming. Here, we separate out the influence of global climate variability on regional climate variability and identify the specific internal variability which is responsible for the multidecadal cooling trend in the analyzed region. Furthermore, we investigate the specific physical mechanism driving the cooling trend and analyze the role of Atlantic multidecadal oscillation (AMO) in its negative phase. The covariability is − 0.74, i.e., AMO accounts for ~ 55% of total variance in the multidecadal variability. In the negative phase of AMO, strong signals of Rossby waves emanating from North Atlantic Ocean propagate across the Eurasian continent, and in the latter half of the twentieth century, the effect of this cold sea surface temperature (SST) anomaly is felt in the Central Indian landmass (particularly over IG river basin) through teleconnection. This study will increase the predictability of multidecadal variability in SAT during summer monsoon season over the Central Indian region with AMO as a strong driving component.