Bellomo, K., A. C. Clement, L. N. Murphy, L. M. Polvani and M. A. Cane, 2016: New observational evidence for a positive cloud feedback that amplifies the Atlantic Multidecadal Oscillation. Geophysical Research Letters, 43(18): 9852-9859.
The Atlantic Multidecadal Oscillation (AMO) affects climate variability in the North Atlantic basin and adjacent continents with potential societal impacts. Previous studies based on model simulations and short-term satellite retrievals hypothesized an important role for cloud radiative forcing in modulating the persistence of the AMO in the tropics, but this mechanism remains to be tested with long-term observational records. Here we analyze data sets that span multiple decades and present new observational evidence for a positive feedback between total cloud amount, sea surface temperature (SST), and atmospheric circulation that can strengthen the persistence and amplitude of the tropical branch of the AMO. In addition, we estimate cloud amount feedback from observations and quantify its impact on SST with idealized modeling experiments. From these experiments we conclude that cloud feedbacks can account for 10% to 31% of the observed SST anomalies associated with the AMO over the tropics.
Boer, G. J., D. M. Smith, C. Cassou, F. Doblas-Reyes, G. Danabasoglu, B. Kirtman, Y. Kushnir, M. Kimoto, G. A. Meehl, R. Msadek, W. A. Mueller, K. E. Taylor, F. Zwiers, M. Rixen, Y. Ruprich-Robert and R. Eade, 2016: The Decadal Climate Prediction Project (DCPP) contribution to CMIP6. Geoscientific Model Development, 9(10): 3751-3777.
The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from the fifth Coupled Model Intercomparison Project (CMIP5) and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as a contribution to CMIP6 (Eyring et al., 2016) and to the WCRP Grand Challenge on Near Term Climate Prediction (Kushnir et al., 2016). The DCPP consists of three components. Component A comprises the production and analysis of an extensive archive of retrospective forecasts to be used to assess and understand historical decadal prediction skill, as a basis for improvements in all aspects of end-to-end decadal prediction, and as a basis for forecasting on annual to decadal timescales. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-model forecasts as a basis for potential operational forecast production. Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (e.g. the "hiatus", volcanoes), including the study of the mechanisms that determine these behaviours. Groups are invited to participate in as many or as few of the components of the DCPP, each of which are separately prioritized, as are of interest to them.
Camargo, S. J., A. H. Sobel, A. D. Delgenio, J. A. Jonas, M. Kelley, Y. Lu, D. A. Shaevitz and N. Henderson, 2016: Tropical cyclones in the GISS ModelE2. Tellus Series a-Dynamic Meteorology and Oceanography, 68.
The authors describe the characteristics of tropical cyclone (TC) activity in the GISS general circulation ModelE2 with a horizontal resolution 1 degrees x1 degrees. Four model simulations are analysed. In the first, the model is forced with sea surface temperature (SST) from the recent historical climatology. The other three have different idealised climate change simulations, namely (1) a uniform increase of SST by 2 degrees, (2) doubling of the CO2 concentration and (3) a combination of the two. These simulations were performed as part of the US Climate Variability and Predictability Program Hurricane Working Group. Diagnostics of standard measures of TC activity are computed from the recent historical climatological SST simulation and compared with the same measures computed from observations. The changes in TC activity in the three idealised climate change simulations, by comparison with that in the historical climatological SST simulation, are also described. Similar to previous results in the literature, the changes in TC frequency in the simulation with a doubling CO2 and an increase in SST are approximately the linear sum of the TC frequency in the other two simulations. However, in contrast with previous results, in these simulations the effects of CO2 and SST on TC frequency oppose each other. Large-scale environmental variables associated with TC activity are then analysed for the present and future simulations. Model biases in the large-scale fields are identified through a comparison with ERA-Interim reanalysis. Changes in the environmental fields in the future climate simulations are shown and their association with changes in TC activity discussed.
Chen, C., M. A. Cane, N. Henderson, D. E. Lee, D. Chapman, D. Kondrashov and M. D. Chekroun, 2016: Diversity, Nonlinearity, Seasonality, and Memory Effect in ENSO Simulation and Prediction Using Empirical Model Reduction. Journal of Climate, 29(5): 1809-1830.
A suite of empirical model experiments under the empirical model reduction framework are conducted to advance the understanding of ENSO diversity, nonlinearity, seasonality, and the memory effect in the simulation and prediction of tropical Pacific sea surface temperature (SST) anomalies. The model training and evaluation are carried out using 4000-yr preindustrial control simulation data from the coupled model GFDL CM2.1. The results show that multivariate models with tropical Pacific subsurface information and multilevel models with SST history information both improve the prediction skill dramatically. These two types of models represent the ENSO memory effect based on either the recharge oscillator or the time-delayed oscillator viewpoint. Multilevel SST models are a bit more efficient, requiring fewer model coefficients. Nonlinearity is found necessary to reproduce the ENSO diversity feature for extreme events. The nonlinear models reconstruct the skewed probability density function of SST anomalies and improve the prediction of the skewed amplitude, though the role of nonlinearity may be slightly overestimated given the strong nonlinear ENSO in GFDL CM2.1. The models with periodic terms reproduce the SST seasonal phase locking but do not improve the prediction appreciably. The models with multiple ingredients capture several ENSO characteristics simultaneously and exhibit overall better prediction skill for more diverse target patterns. In particular, they alleviate the spring/autumn prediction barrier and reduce the tendency for predicted values to lag the target month value.
Gille, S. T., D. C. McKee and D. G. Martinson, 2016: Temporal Changes in the Antarctic Circumpolar Current IMPLICATIONS FOR THE ANTARCTIC CONTINENTAL SHELVES. Oceanography, 29(4): 96-105.
Some of the most rapid melting of ice sheets and ice shelves around Antarctica has occurred where the Antarctic Circumpolar Current (ACC) is in close proximity to the Antarctic continent. Several mechanisms have been hypothesized by which warming trends in the ACC could lead to warmer temperatures on the Antarctic continental shelves and corresponding thinning of ice shelves. One possibility is that a southward shift in the dominant westerly winds has led to a southward shift in the ACC, bringing comparatively warm (1 degrees C-3 degrees C) Circumpolar Deep Water (CDW) in closer contact with Antarctica; however, satellite altimetry does not provide strong evidence for this option. A second possibility is that stronger winds have led to stronger poleward eddy heat transport, bringing more CDW southward. In addition, submarine canyons and winds are hypothesized to be critical for transporting CDW across the continental shelves. The specific mechanisms and the relative roles of westerly winds, easterly winds, and wind-stress curl remain areas of active research.
Han, R. Q., H. Wang, Z. Z. Hu, A. Kumar, W. J. Li, L. N. Long, J. K. E. Schemm, P. T. Peng, W. Q. Wang, D. Si, X. L. Jia, M. Zhao, G. A. Vecchi, T. E. Larow, Y. K. Lim, S. D. Schubert, S. J. Camargo, N. Henderson, J. A. Jonas and K. J. E. Walsh, 2016: An Assessment of Multimodel Simulations for the Variability of Western North Pacific Tropical Cyclones and Its Association with ENSO. Journal of Climate, 29(18): 6401-6423.
An assessment of simulations of the interannual variability of tropical cyclones (TCs) over the western North Pacific (WNP) and its association with El Nino-Southern Oscillation (ENSO), as well as a subsequent diagnosis for possible causes of model biases generated from simulated large-scale climate conditions, are documented in the paper. The model experiments are carried out by the Hurricane Work Group under the U.S. Climate Variability and Predictability Research Program (CLIVAR) using five global climate models (GCMs) with a total of 16 ensemble members forced by the observed sea surface temperature and spanning the 28-yr period from 1982 to 2009. The results show GISS and GFDL model ensemble means best simulate the interannual variability of TCs, and the multimodel ensemble mean (MME) follows. Also, the MME has the closest climate mean annual number of WNP TCs and the smallest root-mean-square error to the observation.
Kim, H., S. C. Doney, R. A. Iannuzzi, M. P. Meredith, D. G. Martinson and H. W. Ducklow, 2016: Climate forcing for dynamics of dissolved inorganic nutrients at Palmer Station, Antarctica: An interdecadal (1993-2013) analysis. Journal of Geophysical Research-Biogeosciences, 121(9): 2369-2389.
We analyzed 20years (1993-2013) of observations of dissolved inorganic macronutrients (nitrate, N; phosphate, P; and silicate, Si) and chlorophyll a (Chl) at Palmer Station, Antarctica (64.8 degrees S, 64.1 degrees W) to elucidate how large-scale climate and local physical forcing affect the interannual variability in the seasonal phytoplankton bloom and associated drawdown of nutrients. The leading modes of nutrients (N, P, and Si empirical orthogonal functions 1, EOF1) represent overall negative anomalies throughout growing seasons, showing a mixed signal of variability in the initial levels and drawdown thereafter (low-frequency dynamics). The second most common seasonal patterns of nitrate and phosphate (N and P EOF2) capture prolonged drawdown events during December-March, which are correlated to Chl EOF1. Si EOF2 captures a drawdown event during November-December, which is correlated to Chl EOF2. These different drawdown patterns are shaped by different sets of physical and climate forcing mechanisms. N and P drawdown events during December-March are influenced by the winter and spring Southern Annular Mode (SAM) phase, where nutrient utilization is enhanced in a stabilized upper water column as a consequence of SAM-driven winter sea ice and spring wind dynamics. Si drawdown during November-December is influenced by early sea ice retreat, where ice breakup may induce abrupt water column stratification and a subsequent diatom bloom or release of diatom cells from within the sea ice. Our findings underscore that seasonal nutrient dynamics in the coastal WAP are coupled to large-scale climate forcing and related physics, understanding of which may enable improved projections of biogeochemical responses to climate change.
Lee, D. E., D. Chapman, N. Henderson, C. Chen and M. A. Cane, 2016: Multilevel vector autoregressive prediction of sea surface temperature in the North Tropical Atlantic Ocean and the Caribbean Sea. Climate Dynamics, 47(1-2): 95-106.
We use a multilevel vector autoregressive model (VAR-L), to forecast sea surface temperature anomalies (SSTAs) in the Atlantic hurricane Main Development Region (MDR). VAR-L is a linear regression model using global SSTA data from L prior months as predictors. In hindcasts for the recent 30 years, the multilevel VAR-L outperforms a state-of-the-art dynamic forecast model, as well as the commonly used linear inverse model (LIM). The multilevel VAR-L model shows skill in 6-12 month forecasts, with its greatest skill in the months of the active hurricane season. The optimized model for the best long-range skill score in the MDR, chosen by a cross-validation procedure, has 12 time levels and 12 empirical orthogonal function modes. We investigate the optimal initial conditions for MDR SSTA prediction using a generalized singular vector decomposition of the propagation matrix. We find that the added temporal degrees of freedom for the predictands in VAR12 as compared with a LIM model, which allow the model to capture both the local wind-evaporation-SST feedback in the Tropical Atlantic and the impact on the Atlantic of an improved medium-range ENSO forecast, elevate the long-range forecast skill in the MDR.
Mascioli, N. R., A. M. Fiore, M. Previdi and G. Correa, 2016: Temperature and Precipitation Extremes in the United States: Quantifying the Responses to Anthropogenic Aerosols and Greenhouse Gases(,+). Journal of Climate, 29(7): 2689-2701.
Changes in extreme temperatures, heat waves, and heavy rainfall events have adverse effects on human health, air quality, and water resources. With aerosol-only (AER) and greenhouse gas-only (GHG) simulations from 1860 to 2005 in the GFDL CM3 chemistry-climate model, aerosol-induced versus greenhouse gas-induced changes in temperature (summer) and precipitation (all seasons) extremes over the United States are investigated. Small changes in these extremes in the all forcing simulations reflect cancellations between the effects of increasing anthropogenic aerosols and greenhouse gases. In AER, extreme high temperatures and the number of days with temperatures above the 90th percentile decline over most of the United States. The strongest response occurs in the western United States (-2.0 degrees C and -14 days, regionally averaged) and the weakest response occurs in the southeastern United States (-0.6 degrees C and -4.8 days). An opposite-signed response pattern occurs in GHG (+2.3 degrees C and +11.5 days over the western United States and +1.6 degrees C and +7.2 days over the southeastern United States). The similar spatial response patterns in AER versus GHG suggest a preferred regional mode of response that is largely independent of the type of forcing. Extreme precipitation over the eastern United States decreases in AER, particularly in winter, and increases over the eastern and central United States in GHG, particularly in spring. Over the twenty-first century under the representative concentration pathway 8.5 (RCP8.5) emissions scenario, the patterns of extreme temperature and precipitation associated with greenhouse gas forcing dominate.
Pomposi, C., A. Giannini, Y. Kushnir and D. E. Lee, 2016: Understanding Pacific Ocean influence on interannual precipitation variability in the Sahel. Geophysical Research Letters, 43(17): 9234-9242.
Moisture budget decomposition is performed for the Sahel (10 degrees-20 degrees N and 20 degrees W-40 degrees E) in order to understand the processes that govern regional hydroclimate variability on interannual time scales and frame them in the context of their primary ocean driver. Results show that warm conditions in the Eastern Tropical Pacific remotely force anomalously dry conditions primarily through affecting the low-troposphere mass divergence field. This behavior is related to increased subsidence over the tropical Atlantic and into the Sahel and an anomalous westward flow of moisture from the continent, both resulting in a coherent drying pattern. Understanding the physical processes relating remote sea surface temperature anomalies to atmospheric circulation changes and the resulting complex local convergence patterns is important for advancing seasonal prediction of precipitation over West Africa.
Schubert, S. D., R. E. Stewart, H. L. Wang, M. Barlow, E. H. Berbery, W. J. Cai, M. P. Hoerling, K. K. Kanikicharla, R. D. Koster, B. Lyon, A. Mariotti, C. R. Mechoso, O. V. Muller, B. Rodriguez-Fonseca, R. Seager, S. I. Senevirante, L. X. Zhang and T. J. Zhou, 2016: Global Meteorological Drought: A Synthesis of Current Understanding with a Focus on SST Drivers of Precipitation Deficits. Journal of Climate, 29(11): 3989-4019, DOI: 10.1175/JCLI-D-15-0452.1.
Drought affects virtually every region of the world, and potential shifts in its character in a changing climate are a major concern. This article presents a synthesis of current understanding of meteorological drought, with a focus on the large-scale controls on precipitation afforded by sea surface temperature (SST) anomalies, land surface feedbacks, and radiative forcings. The synthesis is primarily based on regionally focused articles submitted to the Global Drought Information System (GDIS) collection together with new results from a suite of atmospheric general circulation model experiments intended to integrate those studies into a coherent view of drought worldwide. On interannual time scales, the preeminence of ENSO as a driver of meteorological drought throughout much of the Americas, eastern Asia, Australia, and the Maritime Continent is now well established, whereas in other regions (e.g., Europe, Africa, and India), the response to ENSO is more ephemeral or nonexistent. Northern Eurasia, central Europe, and central and eastern Canada stand out as regions with few SST-forced impacts on precipitation on interannual time scales. Decadal changes in SST appear to be a major factor in the occurrence of long-term drought, as highlighted by apparent impacts on precipitation of the late 1990s "climate shifts" in the Pacific and Atlantic SST. Key remaining research challenges include (i) better quantification of unforced and forced atmospheric variability as well as land-atmosphere feedbacks, (ii) better understanding of the physical basis for the leading modes of climate variability and their predictability, and (iii) quantification of the relative contributions of internal decadal SST variability and forced climate change to long-term drought.
Simpson, I., R. Seager, M. Ting and T. Shaw, 2016: Causes of change in Northern Hemisphere winter meridional wind and regional hydroclimate. Nature Clim. Change, 6: 65-70, DOI: 10.1038/NCLIMATE2783.
A critical aspect of human-induced climate change is how it will affect precipitation around the world. Broadly speaking, warming increases atmospheric moisture holding capacity, intensifies moisture transports and makes sub-tropical dry regions drier and tropical and mid-to-high-latitudewet regions wetter(1,2). Extra-tropical precipitation patterns vary strongly with longitude, however, owing to the control exerted by the storm tracks and quasi-stationary highs and lows or stationary waves. Regional precipitation change will, therefore, also depend on how these aspects of the circulation respond. Current climate models robustly predict a change in the Northern Hemisphere (NH) winter stationary wave field that brings wetting southerlies to the west coast of North America, and drying northerlies to interior southwest North America and the eastern Mediterranean(3-5). Here we show that this change in the meridional wind field is caused by strengthened zonal mean westerlies in the sub-tropical upper troposphere, which alters the character of intermediate-scale stationary waves. Thus, a robust and easily understood model response to global warming is the prime cause of these regional wind changes. However, the majority of models probably overestimate the magnitude of this response because of biases in their climatological representation of the relevant waves, suggesting that winter season wetting of the North American west coast will be notably less than projected by the multi-model mean.
Wang, L., M. F. Ting, D. Chapman, D. E. Lee, N. Henderson and X. J. Yuan, 2016: Prediction of northern summer low-frequency circulation using a high-order vector auto-regressive model. Climate Dynamics, 46(3-4): 693-709.
A data-driven, high-order vector auto-regressive (VAR) model is evaluated for predicting the Northern Hemisphere summer time (May through September) low frequency (> 10 days or so) variability. The VAR model is suitable for linear stationary time series, similar to the commonly used linear inverse model (LIM), with additional temporal information incorporated to improve forecast skill. The intraseasonal forecast skill of the 250/750 hPa streamfunction is investigated using observational data since 1979, which shows significant improvements in high-order VAR models than the first-order model LIM. Furthermore, the tropical diabatic heating is found to significantly improve the forecast skill of the atmospheric low frequency circulation when included in the VAR model. The forecast skill of 250 hPa streamfunction at Arabian Peninsula is particularly enhanced for up to 5 weeks lead-time through circumglobal wave propagation associated with the persistent tropical eastern Pacific and equatorial Atlantic heating anomalies and the intraseasonal evolution of the tropical Indian Ocean and western Pacific heating anomalies.
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