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E. Adam Paxton, Matthew Chantry, Milan Klöwer, Leo Saffin, and Tim Palmer

Abstract

Motivated by recent advances in operational weather forecasting, we study the efficacy of low-precision arithmetic for climate simulations. We develop a framework to measure rounding error in a climate model, which provides a stress test for a low-precision version of the model, and we apply our method to a variety of models including the Lorenz system, a shallow water approximation for flow over a ridge, and a coarse-resolution spectral global atmospheric model with simplified parameterizations (SPEEDY). Although double precision [52 significant bits (sbits)] is standard across operational climate models, in our experiments we find that single precision (23 sbits) is more than enough and that as low as half precision (10 sbits) is often sufficient. For example, SPEEDY can be run with 12 sbits across the code with negligible rounding error, and with 10 sbits if minor errors are accepted, amounting to less than 0.1 mm (6 h)−1 for average gridpoint precipitation, for example. Our test is based on the Wasserstein metric and this provides stringent nonparametric bounds on rounding error accounting for annual means as well as extreme weather events. In addition, by testing models using both round-to-nearest (RN) and stochastic rounding (SR) we find that SR can mitigate rounding error across a range of applications, and thus our results also provide some evidence that SR could be relevant to next-generation climate models. Further research is needed to test if our results can be generalized to higher resolutions and alternative numerical schemes. However, the results open a promising avenue toward the use of low-precision hardware for improved climate modeling.

Significance Statement

Weather and climate models provide vital information for decision-making, and will become ever more important in the future with a changed climate and more extreme weather. A central limitation to improved models are computational resources, which is why some weather forecasters have recently shifted from conventional 64-bit to more efficient 32-bit computations, which can provide equally accurate forecasts. Climate models, however, still compute in 64 bits, and adapting to lower precision requires a detailed analysis of rounding errors. We develop methods to quantify rounding error in a climate model, and find similar precision acceptable across weather and climate models, with even 16 bits often sufficient for an accurate climate. This opens a promising avenue for computational efficiency gains in climate modeling.

Open access
Antoine Hochet, Thierry Huck, Olivier Arzel, Florian Sévellec, and Alain Colin de Verdière

Abstract

One of the proposed mechanisms to explain the multidecadal variability observed in sea surface temperature of the North Atlantic Ocean consists of a large-scale low-frequency internal mode spontaneously developing because of the large-scale baroclinic instability of the time-mean circulation. Even though this mode has been extensively studied in terms of the buoyancy variance budget, its energetic properties remain poorly known. Here we perform the full mechanical energy budget including available potential energy (APE) and kinetic energy (KE) of this internal mode and decompose the budget into three frequency bands: mean, low frequency (LF) associated with the large-scale mode, and high frequency (HF) associated with mesoscale eddy turbulence. This decomposition allows us to diagnose the energy fluxes between the different reservoirs and to understand the sources and sinks. Because of the large scale of the mode, most of its energy is contained in the APE. In our configuration, the only source of LF APE is the transfer from mean APE to LF APE that is attributed to the large-scale baroclinic instability. In return the sinks of LF APE are the parameterized diffusion, the flux toward HF APE, and, to a much lesser extent, the flux toward LF KE. The presence of an additional wind stress component weakens multidecadal oscillations and modifies the energy fluxes between the different energy reservoirs. The KE transfer appears to only have a minor influence on the multidecadal mode relative to the other energy sources involving APE, in all experiments. These results highlight the utility of the full APE–KE budget.

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Alex O. Gonzalez, Indrani Ganguly, Marie C. McGraw, and James G. Larson

Abstract

The latitudinal location of the east Pacific Ocean intertropical convergence zone (ITCZ) changes on time scales of days to weeks during boreal spring. This study focuses on tropical near-surface dynamics in the days leading up to the two most frequent types of ITCZ events, nITCZ (Northern Hemisphere) and dITCZ (double). There is a rapid daily evolution of dynamical features on top of a slower, weekly evolution that occurs leading up to and after nITCZ and dITCZ events. Zonally elongated bands of anomalous cross-equatorial flow and off-equatorial convergence rapidly intensify and peak 1 day before or the day of these ITCZ events, followed 1 or 2 days later by a peak in near-equatorial zonal wind anomalies. In addition, there is a wide region north of the southeast Pacific subtropical high where anomalous northwesterlies strengthen prior to nITCZ events and southeasterlies strengthen before dITCZ events. Anomalous zonal and meridional near-surface momentum budgets reveal that the terms associated with Ekman balance are of first-order importance preceding nITCZ events, but that the meridional momentum advective terms are just as important before dITCZ events. Variations in cross-equatorial flow are promoted by the meridional pressure gradient force (PGF) prior to nITCZ events and the meridional advection of meridional momentum in addition to the meridional PGF before dITCZ events. Meanwhile, variations in near-equatorial easterlies are driven by the zonal PGF and the Coriolis force preceding nITCZ events and the zonal PGF, the Coriolis force, and the meridional advection of zonal momentum before dITCZ events.

Open access
M. Kathleen Brennan and Gregory J. Hakim

Abstract

Arctic sea ice decline in recent decades has been dramatic; however, few long-term records of Arctic sea ice exist to put such a decline in context. Here we employ an ensemble Kalman filter data assimilation approach to reconstruct Arctic sea ice concentration over the last two millennia by assimilating temperature-sensitive proxy records with ensembles drawn from last millennium climate model simulations. We first test the efficacy of this method using pseudoproxy experiments. Results show good agreement between the target and reconstructed total Arctic sea ice extent (R 2 value and coefficient of efficiency values of 0.51 and 0.47 for perfect model experiments and 0.43 and 0.43 for imperfect model experiments). Imperfect model experiments indicate that the reconstructions inherit some bias from the model prior. We assimilate 487 temperature-sensitive proxy records with two climate model simulations to produce two gridded reconstructions of Arctic sea ice over the last two millennia. These reconstructions show good agreement with satellite observations between 1979 and 1999 CE for total Arctic sea ice extent with an R 2 value and coefficient of efficiency of about 0.60 and 0.50, respectively, for both models. Regional quantities derived from these reconstructions show encouraging similarities with independent reconstructions and sea ice sensitive proxy records from the Barents Sea, Baffin Bay, and East Greenland Sea. The reconstructions show a positive trend in Arctic sea ice extent between around 750 and 1820 CE, and increases during years with large volcanic eruptions that persist for about 5 years. Trend analysis of total Arctic sea ice extent reveals that for time periods longer than 30 years, the satellite era decline in total Arctic sea ice extent is unprecedented over the last millennium.

Significance Statement

Areal coverage of Arctic sea ice is a critical aspect of the climate system that has been changing rapidly in recent decades. Prior to the advent of satellite observations, sparse observations of Arctic sea ice make it difficult to put the current changes in context. Here we reconstruct annual averages of Arctic sea ice coverage for the last two millennia by combining temperature-sensitive proxy records (i.e., ice cores, tree rings, and corals) with climate model simulations using a statistical technique called data assimilation. We find large interannual changes in Arctic sea ice coverage prior to 1850 that are associated with volcanic eruptions, with a steady rise in Arctic sea ice coverage between 750 and 1820 CE. The satellite-period loss of sea ice has no analog during the last millennium.

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Marie C. McGraw, Eduardo Blanchard-Wrigglesworth, Robin P. Clancy, and Cecilia M. Bitz

Abstract

The predictability of sea ice during extreme sea ice loss events on subseasonal (daily to weekly) time scales is explored in dynamical forecast models. These extreme sea ice loss events (defined as the 5th percentile of the 5-day change in sea ice extent) exhibit substantial regional and seasonal variability; in the central Arctic Ocean basin, most subseasonal rapid ice loss occurs in the summer, but in the marginal seas rapid sea ice loss occurs year-round. Dynamical forecast models are largely able to capture the seasonality of these extreme sea ice loss events. In most regions in the summertime, sea ice forecast skill is lower on extreme sea ice loss days than on nonextreme days, despite evidence that links these extreme events to large-scale atmospheric patterns; in the wintertime, the difference between extreme and nonextreme days is less pronounced. In a damped anomaly forecast benchmark estimate, the forecast error remains high following extreme sea ice loss events and does not return to typical error levels for many weeks; this signal is less robust in the dynamical forecast models but still present. Overall, these results suggest that sea ice forecast skill is generally lower during and after extreme sea ice loss events and also that, while dynamical forecast models are capable of simulating extreme sea ice loss events with similar characteristics to what we observe, forecast skill from dynamical models is limited by biases in mean state and variability and errors in the initialization.

Significance Statement

We studied weather model forecasts of changes in Arctic sea ice extent on day-to-day time scales in different regions and seasons. We were especially interested in extreme sea ice loss days, or days in which sea ice melts very quickly or is reduced due to diverging forces such as winds, ocean currents, and waves. We find that forecast models generally capture the observed timing of extreme sea ice loss days. We also find that forecasts of sea ice extent are worse on extreme sea ice loss days compared to typical days, and that forecast errors remain elevated following extreme sea ice loss events.

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Hiroaki Ueda, Masaya Kuramochi, Koutarou Takaya, Yuhei Takaya, Saki Asano, and Shuhei Maeda

Abstract

Upper-tropospheric anticyclones (UTACs) emerge throughout the seasons with changing location and intensity. Here, the formation mechanisms of these UTACs, especially in the Asian–Australian–western Pacific sector, were investigated based on the diagnosis of the vorticity equation as well as the contribution of the planetary waves. During June–July–August (JJA), a vigorous UTAC corresponding to the South Asian high (SAH) forms over South Asia, to the south of the Tibetan Plateau, where intense heating associated with the Asian summer monsoon rainfall and the resultant baroclinic Rossby response are the important physical processes. Meanwhile, the produced anticyclonic vorticity is farther transported by the interhemispheric divergent wind toward the Southern Hemisphere (SH), creating the SH UTAC centered over the Maritime Continent. During December–January–February (DJF), two zonally elongated UTACs reside on each side of the equator (∼10° poleward), mainly over the Maritime Continent–western Pacific sector. Upon a closer look at the NH winter, we observed that the northern parts of UTAC cannot be explained by this vorticity balance alone. Diagnosis of the wave activity flux indicated that planetary waves emanating from the cold Eurasian continent converges around the northern parts of the UTAC with its peak in the NH winter, which weakens the subtropical jet, thus generating UTAC. Configuration of the SH summer (DJF) UTAC bears resemblance with that of SAH. These results suggest that the creation of anticyclonic vorticity and its interhemispheric transportation as well as the propagation of planetary wave are the selectively important agents for the genesis of seasonally varying UTACs.

Significance Statement

Recent studies have provided evidence that the South Asian high (formerly Tibetan high) is not a purely thermally driven system only maintained over the elevated Tibetan Plateau. This study aims to understand the physical processes responsible for the genesis of the upper-tropospheric anticyclone, especially in the Asian–Australian–western Pacific sector, throughout the season. During summer in the Northern Hemisphere, deep heating caused by South Asian monsoon rainfall plays a crucial role in the genesis of the South Asian high. The wintertime anticyclone emerging over the subtropical western North Pacific is caused via remote influences anchored with tropical convection and the cold Eurasian continent in which atmospheric teleconnections are important. These findings provide new avenues for research on tropical–extratropical interactions with respect to the formation and variability of important climate phenomena.

Open access
Juncong Li, Zhiping Wen, Xiuzhen Li, and Yuanyuan Guo

Abstract

Interdecadal variations of the relationship between El Niño–Southern Oscillation (ENSO) and the Indo-China Peninsula (ICP) surface air temperature (SAT) in winter are investigated in the study. Generally, there exists a positive correlation between them during 1958–2015 because the ENSO-induced anomalous western North Pacific anticyclone (WNPAC) is conducive to pronounced temperature advection anomalies over the ICP. However, such correlation is unstable in time, having experienced a high-to-low transition around the mid-1970s and a recovery since the early 1990s. This oscillating relationship is owing to the anomalous WNPAC intensity in different decades. During the epoch of high correlation, the anomalous WNPAC and associated southwesterly winds over the ICP are stronger, which brings amounts of warm temperature advection and markedly heats the ICP. In contrast, a weaker WNPAC anomaly and insignificant ICP SAT anomalies are the circumstances for the epoch of low correlation. It is also found that substantial southwesterly wind anomalies over the ICP related to the anomalous WNPAC occur only when large sea surface temperature (SST) anomalies over the northwest Indian Ocean (NWIO) coincide with ENSO (viz., when the ENSO–NWIO SST connection is strong). The NWIO SST anomalies are capable of driving favorable atmospheric circulation that effectively alters ICP SAT and efficiently modulates the ENSO–ICP SAT correlation, which is further supported by numerical simulations utilizing the Community Atmospheric Model, version 4 (CAM4). This paper emphasizes the non-stationarity of the ENSO–ICP SAT relationship and also uncovers the underlying modulation factors, which has important implications for the seasonal prediction of the ICP temperature.

Restricted access
Zixiang Yan, Bo Wu, Tim Li, and Guirong Tan

Abstract

The longitudinal location of precipitation anomalies over the equatorial Pacific shows a distinctive feature with the westernmost location for La Niña, the easternmost location for eastern Pacific (EP) El Niño, and somewhere between for central Pacific (CP) El Niño, even though the center of the sea surface temperature anomaly (SSTA) for La Niña is located slightly east of that of CP El Niño. The mechanisms for such a precipitation diversity were investigated through idealized model simulations and moisture and moist static energy budget analyses. It is revealed that the boundary layer convergence anomalies associated with the precipitation diversity are mainly induced by underlying SSTA through the Lindzen–Nigam mechanism, that is, their longitudinal locations are mainly controlled by the meridional and zonal distributions of the ENSO SSTA. The westward shift of the precipitation anomaly center during La Niña relative to that during CP El Niño is primarily caused by the combined effects of nonlinear zonal moist enthalpy advection anomalies and the Lindzen–Nigam mechanism mentioned above. Such a zonal diversity is further enhanced by the “convection–cloud–longwave radiation” feedback, the SST-induced latent heat flux anomalies, and the advection of mean moist enthalpy by anomalous winds. This diversity in the longitudinal location of precipitation anomalies has contributions to the diversities in the longitudinal locations of anomalous Walker circulation and western North Pacific anomalous anticyclone/cyclone among the three types of ENSO.

Open access
Guiwan Chen, Jian Ling, Yuanwen Zhang, Xin Wang, and Chongyin Li

Abstract

This study explores the impacts of background states on the propagation of the Madden–Julian oscillation (MJO) in 24 CMIP5 models using a precipitation-based MJO tracking method. The ability of the model to reproduce the MJO propagation is reflected in the occurrence frequency of individual MJO events. Moisture budget analysis suggests that the occurrence frequencies of MJO events that propagate across the Indian Ocean (IO-MJO) and western Pacific (WP-MJO) in the models are mainly related to the low-level meridional moisture advection ahead of the MJO convection center. This advection is tightly associated with the background distribution of low-level moisture. Drier biases in background low-level moisture over the entire tropical regions account for underestimated MJO occurrence frequency in the bottom-tier simulations. This study highlights the importance of reproducing the year-to-year background states for the simulations of MJO propagation in the models by further decomposing the background states into the climatology and anomaly components. The background meridional moisture gradient accounting for the IO-MJO occurrence frequency is closely related to its climatology component; however, the anomaly component regulated by El Niño–Southern Oscillation (ENSO) is also important for the WP-MJO occurrence frequency. The year-to-year variations of background zonal and meridional gradients associated with ENSO account for the IO-MJO occurrence frequency tend to be offset from each other. As a result, ENSO shows no significant impact on the IO-MJO occurrence frequency. However, the MJO events are more likely to propagate across the western Pacific during El Niño years.

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Jingfang Fan, Jun Meng, Josef Ludescher, Zhaoyuan Li, Elena Surovyatkina, Xiaosong Chen, Jürgen Kurths, and Hans Joachim Schellnhuber

Abstract

Despite the development of sophisticated statistical and dynamical climate models, a relative long-term and reliable prediction of the Indian summer monsoon rainfall (ISMR) has remained a challenging problem. Toward achieving this goal, here we construct a series of dynamical and physical climate networks based on the global near-surface air temperature field. We show that some characteristics of the directed and weighted climate networks can serve as efficient long-term predictors for ISMR forecasting. The developed prediction method produces a forecasting skill of 0.54 (Pearson correlation) with a 5-month lead time by using the previous calendar year’s data. The skill of our ISMR forecast is better than that of operational forecasts models, which have, however, quite a short lead time. We discuss the underlying mechanism of our predictor and associate it with network–ENSO and ENSO–monsoon connections. Moreover, our approach allows predicting the all-India rainfall, as well as the rainfall different homogeneous Indian regions, which is crucial for agriculture in India. We reveal that global warming affects the climate network by enhancing cross-equatorial teleconnections between the southwest Atlantic, the western part of the Indian Ocean, and the North Asia–Pacific region, with significant impacts on the precipitation in India. A stronger connection through the chain of the main atmospheric circulations patterns benefits the prediction of the amount of rainfall. We uncover a hotspot area in the midlatitude South Atlantic, which is the basis for our predictor, the southwest Atlantic subtropical index (SWAS index). Remarkably, the significant warming trend in this area yields an improvement of the prediction skill.

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