Bibliography - Nathaniel C Johnson

1. Ding, Q, A Schweiger, M L L'Heureux, E J Steig, D S Battisti, Nathaniel C Johnson, E Blanchard-Wrigglesworth, S Po-Chedley, Q Zhang, K J Harnos, and Mitchell Bushuk, et al., January 2019: Fingerprints of internal drivers of Arctic sea ice loss in observations and model simulations. Nature Geoscience, 12(1), DOI:10.1038/s41561-018-0256-8.
   Abstract

   The relative contribution and physical drivers of internal variability in recent Arctic sea ice loss remain open questions, leaving up for debate whether global climate models used for climate projection lack sufficient sensitivity in the Arctic to climate forcing. Here, through analysis of large ensembles of fully coupled climate model simulations with historical radiative forcing, we present an important internal mechanism arising from low-frequency Arctic atmospheric variability in models that can cause substantial summer sea ice melting in addition to that due to anthropogenic forcing. This simulated internal variability shows a strong similarity to the observed Arctic atmospheric change in the past 37 years. Through a fingerprint pattern matching method, we estimate that this internal variability contributes to about 40–50% of observed multi-decadal decline in Arctic sea ice. Our study also suggests that global climate models may not actually underestimate sea ice sensitivities in the Arctic, but have trouble fully replicating an observed linkage between the Arctic and lower latitudes in recent decades. Further improvements in simulating the observed Arctic–global linkage are thus necessary before the Arctic’s sensitivity to global warming in models can be quantified with confidence.

2. Fučkar, N S., V Guemas, Nathaniel C Johnson, and F Doblas-Reyes, March 2019: Dynamical prediction of Arctic sea ice modes of variability. Climate Dynamics, 52(5-6), DOI:10.1007/s00382-018-4318-9.
   Abstract

   This study explores the prediction skill of the northern hemisphere (NH) sea ice thickness (SIT) modes of variability in a state-of-the-art coupled forecast system with respect to two statistical forecast benchmarks. Application of the K-means clustering method on a historical reconstruction of SIT from 1958 to 2013, produced by an ocean-sea-ice general circulation model, identifies three Arctic SIT clusters or modes of climate variability. These SIT modes have consistent patterns in different calendar months and their discrete time series of occurrences show persistence on intraseasonal to interannual time scales. We use the EC-Earth2.3 coupled climate model to produce five-member 12-month-long monthly forecasts of the NH SIT modes initialized on 1 May and 1 November every year from 1979 to 2010. We use a three-state first-order Markov chain and climatological probability forecasts determined from the historical SIT mode reconstruction as two statistical reference forecasts. The analysis of ranked probability skill scores (RPSSs) relating these three forecast systems shows that the dynamical SIT mode forecasts typically have a higher skill than the Markov chain forecasts, which are overall better than climatological forecasts. The evolution of RPSS in forecast time indicates that the transition from the sea-ice melting season to growing season in the EC-Earth2.3 forecasts, with respect to the Markov chain model, typically leads to the improvement of prediction skill. The reliability diagrams overall show better reliability of the dynamical forecasts than that of the Markov chain model, especially for 1 May start dates, while dynamical forecasts with 1 November start dates are overconfident. The relative operating characteristics (ROC) diagrams confirm this hierarchy of forecast skill among these three forecast systems. Furthermore, ROC diagrams stratified in groups of 3 sequential forecast months show that Arctic SIT mode forecasts initialized on 1 November typically lose resolution with forecast time more slowly than forecasts initialized on 1 May.

3. L'Heureux, M L., M K Tippett, K Takahashi, A Barnston, E Becker, G D Bell, T E Di Liberto, J Gottschalck, M S Halpert, Z-Z Hu, and Nathaniel C Johnson, et al., February 2019: Strength Outlooks for the El Niño–Southern Oscillation. Weather and Forecasting, 34(1), DOI:10.1175/WAF-D-18-0126.1.
   Abstract

   Three strategies for creating probabilistic forecast outlooks for El Niño–Southern Oscillation (ENSO) are compared. One is subjective and is currently used by the NOAA/Climate Prediction Center (CPC) to produce official ENSO outlooks. A second is purely objective and is based on the North American Multimodel Ensemble (NMME). A new third strategy is proposed in which the forecaster only provides the expected value of the Niño-3.4 index, and then categorical probabilities are objectively determined based on past skill. The new strategy results in more confident probabilities compared to the subjective approach and higher verification scores, while avoiding the significant forecast busts that sometimes afflict the NMME-based objective approach. The higher verification scores of the new strategy appear to result from the added value that forecasters provide in predicting the mean, combined with more reliable representations of uncertainty, which is difficult to represent because forecasters often assume less confidence than is justified. Moreover, the new approach can produce higher-resolution probabilistic forecasts that include ENSO strength information and that are difficult, if not impossible, for forecasters to produce. To illustrate, a nine-category ENSO outlook based on the new strategy is assessed and found to be skillful. The new approach can be applied to other outlooks where users desire higher-resolution probabilistic forecasts, including the extremes.

4. Xiang, Baoqiang, Shian-Jiann Lin, Ming Zhao, Nathaniel C Johnson, Xiaosong Yang, and X Jiang, January 2019: Subseasonal Week 3‐5 Surface Air Temperature Prediction during Boreal Wintertime in a GFDL model. Geophysical Research Letters, 46(1), DOI:10.1029/2018GL081314.
   Abstract

   With a GFDL coupled model, the subseasonal prediction of wintertime (December‐February) surface air temperature (SAT) is investigated through the analysis of 11‐year hindcasts. Significant subseasonal week 3‐5 correlation skill exists over a large portion of the global land domain, and the predictability originates primarily from the eight most predictable SAT modes. The first three modes, identified as the El Niño‐Southern Oscillation mode, the North Atlantic Oscillation (NAO) mode, and the Eurasia Meridional Dipole (EMD) mode, can be skillfully predicted more than 5 weeks in advance. The NAO and EMD modes are strongly correlated with the initial stratospheric polar vortex strength, highlighting the role of stratosphere in subseasonal prediction. Interestingly, the Madden‐Julian Oscillation is not essential for the subseasonal land SAT prediction in the Northern Hemisphere extratropics. The spatial correlation skill exhibits considerable intraseasonal and interannual fluctuations, indicative of the importance to identify the time window of opportunity for subseasonal prediction.

5. He, Jie, Nathaniel C Johnson, Gabriel A Vecchi, B P Kirtman, Andrew T Wittenberg, and S Sturm, November 2018: Precipitation Sensitivity to Local Variations in Tropical Sea Surface Temperature. Journal of Climate, 31(22), DOI:10.1175/JCLI-D-18-0262.1.
   Abstract

   The driving of tropical precipitation by variability of the underlying sea surface temperature (SST) plays a critical role in the atmospheric general circulation. To assess the precipitation sensitivity to SST variability, it is necessary to observe and understand the relationship between precipitation and SST. However, the precipitation – SST relationships from any coupled atmosphere-ocean system can be difficult to interpret due to the challenge of disentangling the SST-forced atmospheric response and the atmospheric intrinsic variability. This study demonstrates that the two components can be isolated using uncoupled atmosphere-only simulations, which extract the former when driven by time-varying SSTs and the latter when driven by climatological SSTs. With a simple framework that linearly combines the two types of uncoupled simulations, the coupled precipitation – SST relationships are successfully reproduced. Such a framework can be a useful tool for quantitatively diagnosing tropical air-sea interactions. The precipitation sensitivity to SST variability is investigated with the use of uncoupled simulations with prescribed SST anomalies, where the influence of atmospheric intrinsic variability on SST is deactivated. Through a focus on local precipitation – SST relationships, the precipitation sensitivity to local SST variability is determined to be predominantly controlled by the local background SST. In addition, the strength of the precipitation response increases monotonically with the local background SST, with a very sharp growth at high SSTs. These findings are supported by basic principles of moist static stability, from which a simple formula for precipitation sensitivity to local SST variability is derived.

6. Janoski, Tyler P., Anthony J Broccoli, Sarah B Kapnick, and Nathaniel C Johnson, November 2018: Effects of Climate Change on Wind-Driven Heavy Snowfall Events over Eastern North America. Journal of Climate, 31(22), DOI:10.1175/JCLI-D-17-0756.1.
   Abstract

   Eastern North America contains densely populated, highly developed areas, making winter storms with strong winds and high snowfall among the costliest storm types. For this reason, it is important to determine how the frequency of high-impact winter storms, specifically those combining significant snowfall and winds, will change in this region under increasing greenhouse gas concentrations. This study uses a high-resolution coupled global climate model to simulate the changes in extreme winter conditions from the present climate to a future scenario with doubled-CO2 concentrations (2XC). In particular, this study focuses on changes in high snowfall, extreme wind (HSEW) events, which are defined as the occurrence of two-day snowfall and high winds exceeding thresholds based on extreme values from the control simulation where greenhouse gas concentrations remain fixed. Mean snowfall consistently decreases across the entire region, but extreme snowfall shows a more inconsistent pattern with some areas experiencing increases in the frequency of extreme snowfall events. Extreme wind events show relatively small changes in frequency with 2XC, with the exception of high-elevation areas where there are large decreases in frequency. As a result of combined changes in wind and snowfall, HSEW events decrease in frequency in the 2XC simulation for much of the eastern North America. Changes in the number of HSEW events in the 2XC environment are driven mainly by changes in the frequency of extreme snowfall events, with most of the region experiencing decreases in event frequency, except for certain inland areas at higher latitudes.

7. Johnson, Nathaniel C., et al., April 2018: Increasing occurrence of cold and warm extremes during the recent global warming slowdown. Nature Communications, 9, 1724, DOI:10.1038/s41467-018-04040-y.
   Abstract

   The recent levelling of global mean temperatures after the late 1990s, the so-called global warming hiatus or slowdown, ignited a surge of scientific interest into natural global mean surface temperature variability, observed temperature biases, and climate communication, but many questions remain about how these findings relate to variations in more societally relevant temperature extremes. Here we show that both summertime warm and wintertime cold extreme occurrences increased over land during the so-called hiatus period, and that these increases occurred for distinct reasons. The increase in cold extremes is associated with an atmospheric circulation pattern resembling the warm Arctic-cold continents pattern, whereas the increase in warm extremes is tied to a pattern of sea surface temperatures resembling the Atlantic Multidecadal Oscillation. These findings indicate that large-scale factors responsible for the most societally relevant temperature variations over continents are distinct from those of global mean surface temperature.

8. Yoo, C, and Nathaniel C Johnson, et al., November 2018: Subseasonal Prediction of Wintertime East Asian Temperature Based on Atmospheric Teleconnections. Journal of Climate, 31(22), DOI:10.1175/JCLI-D-17-0811.1.
   Abstract

   A composite-based statistical model utilizing Northern Hemisphere teleconnection patterns is developed to predict East Asian wintertime surface air temperature for lead times out to 6 weeks. The level of prediction is determined by using the Heidke skill score. The prediction skill of the statistical model is compared with that of hindcast simulations by a climate model, Global Seasonal Forecast System, version 5. When employed individually, three teleconnections (i.e., the east Atlantic/western Russian, Scandinavian, and polar/Eurasian teleconnection patterns) are found to provide skillful predictions for lead times beyond 4–5 weeks. When information from the teleconnections and the long-term linear trend are combined, the statistical model outperforms the climate model for lead times beyond 3 weeks, especially during those times when the teleconnections are in their active phases.

9. Black, J, and Nathaniel C Johnson, et al., July 2017: The Predictors and Forecast Skill of Northern Hemisphere Teleconnection Patterns for Lead Times of 3-4 Weeks. Monthly Weather Review, 145(7), DOI:10.1175/MWR-D-16-0394.1.
   Abstract

   The Pacific/North American (PNA) pattern, North Atlantic Oscillation (NAO), and Arctic Oscillation (AO) are three dominant teleconnection patterns known to strongly affect December-February surface weather in the Northern Hemisphere. A partial least-squares regression (PLSR) method is adopted in this study to generate wintertime 2-week statistical forecasts of these three teleconnection pattern indices for lead times of up to five weeks over the 1980-2013 period. The PLSR approach generates forecasts for the teleconnection pattern indices by maximizing the variance explained by predictor indices determined as linear combinations of predictor fields, which include gridded outgoing longwave radiation (OLR), 300-hPa geopotential height (Z300), and 50-hPa geopotential height (Z50). Overall, the PLSR models yield statistically significant skill at all lead times up to five weeks. In particular, cross-validated correlations between the combined weeks 3-4 PLSR forecasts and verification for the PNA, NAO and AO indices are 0.34, 0.28 and 0.41. The PLSR approach also allows the authors to isolate a small number of predictor patterns that help shed light on the sources of prediction skill for each teleconnnection pattern. As expected, the results reveal the importance of tropical convection (OLR) for forecast skill in weeks 3-4, but the initial atmospheric flow (Z300) accounts for a substantial fraction of the skill as well. Overall, the results of this study provide promise for improving subseasonal-to-seasonal (S2S) forecasts and the physical understanding of predictability on these time scales.

10. Ding, Q, A Schweiger, M L L'Heureux, D S Battisti, S Po-Chedley, and Nathaniel C Johnson, et al., April 2017: Influence of high-latitude atmospheric circulation changes on summertime Arctic sea ice. Nature Climate Change, 7(4), DOI:10.1038/nclimate3241.
    Abstract

    The Arctic has seen rapid sea-ice decline in the past three decades, whilst warming at about twice the global average rate. Yet the relationship between Arctic warming and sea-ice loss is not well understood. Here, we present evidence that trends in summertime atmospheric circulation may have contributed as much as 60% to the September sea-ice extent decline since 1979. A tendency towards a stronger anticyclonic circulation over Greenland and the Arctic Ocean with a barotropic structure in the troposphere increased the downwelling longwave radiation above the ice by warming and moistening the lower troposphere. Model experiments, with reanalysis data constraining atmospheric circulation, replicate the observed thermodynamic response and indicate that the near-surface changes are dominated by circulation changes rather than feedbacks from the changing sea-ice cover. Internal variability dominates the Arctic summer circulation trend and may be responsible for about 30–50% of the overall decline in September sea ice since 1979.

11. L'Heureux, M L., M K Tippett, A Kumar, T Butler, L M Ciasto, Q Ding, K J Harnos, and Nathaniel C Johnson, November 2017: Strong Relations Between ENSO and the Arctic Oscillation in the North American Multimodel Ensemble. Geophysical Research Letters, DOI:10.1002/2017GL074854.
    Abstract

    Arctic Oscillation (AO) variability impacts climate anomalies over the middle to high latitudes of the Northern Hemisphere. Recently, state-of-the-art climate prediction models have proved capable of skillfully predicting the AO during the winter, revealing a previously unrealized source of climate predictability. Hindcasts from the North American Multimodel Ensemble (NMME) show that the seasonal, ensemble mean 200 hPa AO index is skillfully predicted up to 7 months in advance and that this skill, especially at longer leads, is coincident with previously unknown and strong relations (r > 0.9) with the El Niño–Southern Oscillation (ENSO). The NMME is a seasonal prediction system that comprises eight models and up to 100 members with forecasts out to 12 months. Observed ENSO-AO correlations are within the spread of the NMME member correlations, but the majority of member correlations are stronger than observed, consistent with too high predictability in the model, or overconfidence.

12. Johnson, Nathaniel C., and Y Kosaka, December 2016: The impact of eastern equatorial Pacific convection on the diversity of boreal winter El Niño teleconnection patterns. Climate Dynamics, 47(12), DOI:10.1007/s00382-016-3039-1.
    Abstract

    It is widely recognized that no two El Niño episodes are the same; hence the predictable variations of the climate impacts associated with El Niño remain an open problem. Through an analysis of observational data and of large ensembles from six climate models forced by the observed time-varying sea surface temperatures (SSTs), this study raises the argument that the most fundamental predictable variations of boreal wintertime El Niño teleconnection patterns relate to the distinction between convective (EPC) and non-convective eastern Pacific (EPN) events. This distinction is a consequence of the nonlinear relationship between deep convection and eastern Pacific SSTs, and the transition to a convective eastern Pacific has a predictable relationship with local and tropical mean SSTs. Notable differences (EPC minus EPN) between the teleconnection patterns include positive precipitation differences over southern North America and northern Europe, positive temperature differences over northeast North America, and negative temperature differences over the Arctic. These differences are stronger and more statistically significant than the more common partitioning between eastern Pacific and central Pacific El Niño. Most of the seasonal mean composite anomalies associated with EPN El Niño are not statistically significant owing to the weak SST forcing and small sample sizes; however, the EPN teleconnection is more robust on subseasonal timescales following periods when the EPN pattern of tropical convection is active. These findings suggest that the differences between EPC and EPN climate impacts are physically robust and potentially useful for intraseasonal forecasts for lead times of up to a few weeks.

13. Chang, C-H, and Nathaniel C Johnson, December 2015: The Continuum of Wintertime Southern Hemisphere Atmospheric Teleconnection Patterns. Journal of Climate, 28(24), DOI:10.1175/JCLI-D-14-00739.1.
    Abstract

    This study uses the method of self-organizing maps (SOMs) to categorize the June-August atmospheric teleconnections in the 500-hPa geopotential height field of the Southern Hemisphere (SH) extratropics. This approach yields 12 SOM patterns that provide a discretized representation of the continuum of SH teleconnection patterns from 1979 to 2012. These 12 patterns are large in spatial scale, exhibiting a mix of annular mode characteristics and wave trains of zonal wavenumber varying from 2 to 4. All patterns vary with intrinsic time scales of about 5-10 days, but some patterns exhibit quasi-oscillatory behavior over a period of 20-30 days, whereas still others exhibit statistically significant enhanced and suppressed frequencies up to about four weeks in association with the Madden-Julian oscillation. Two patterns are significantly influenced by El Nino-Southern Oscillation (ENSO) on interannual time scales. All 12 patterns have strong influences on surface air temperature and sea ice concentrations, with the sea ice response occurring over a time scale of about 2-4 weeks. The austral winter has featured a positive frequency trend in patterns that project onto the negative phase of the southern annular mode (SAM) and a negative frequency trend in positive SAM-like patterns. Such atmospheric circulation trends over 34 yr may arise through atmospheric internal variability alone, and, unlike other seasons in the SH, it is not necessary to invoke external forcing as a dominant source of circulation trends.

14. Xie, Shang-Ping, C Deser, Gabriel A Vecchi, M Collins, Thomas L Delworth, A Hall, E Hawkins, Nathaniel C Johnson, C Cassou, A Giannini, and M Watanabe, October 2015: Towards predictive understanding of regional climate change. Nature Climate Change, 5(10), DOI:10.1038/nclimate2689.
    Abstract

    Regional information on climate change is urgently needed but often deemed unreliable. To achieve credible regional climate projections, it is essential to understand underlying physical processes, reduce model biases and evaluate their impact on projections, and adequately account for internal variability. In the tropics, where atmospheric internal variability is small compared with the forced change, advancing our understanding of the coupling between long-term changes in upper-ocean temperature and the atmospheric circulation will help most to narrow the uncertainty. In the extratropics, relatively large internal variability introduces substantial uncertainty, while exacerbating risks associated with extreme events. Large ensemble simulations are essential to estimate the probabilistic distribution of climate change on regional scales. Regional models inherit atmospheric circulation uncertainty from global models and do not automatically solve the problem of regional climate change. We conclude that the current priority is to understand and reduce uncertainties on scales greater than 100 km to aid assessments at finer scales.

15. Maloney, E, S J Camargo, E K M Chang, B A Colle, R Fu, K L Geil, Qi Hu, X Jiang, Nathaniel C Johnson, K B Karnauskas, J L Kinter, B P Kirtman, Sanjiv Kumar, B Langenbrunner, K Lombardo, L Long, A Mariotti, J E Meyerson, K Mo, J D Neelin, Zaitao Pan, R Seager, Y L Serra, A Seth, J Sheffield, J Stroeve, J Thibeault, Shang-Ping Xie, Chunzai Wang, Bruce Wyman, and Ming Zhao, March 2014: North American Climate in CMIP5 Experiments: Part III: Assessment of 21st Century Projections. Journal of Climate, 27(6), DOI:10.1175/JCLI-D-13-00273.1.
    Abstract

    In Part 3 of a three-part study on North American climate in Coupled Model Intercomparison project (CMIP5) models, we examine projections of 21st century climate in the RCP8.5 emission experiments. This paper summarizes and synthesizes results from several coordinated studies by the authors. Aspects of North American climate change that are examined include changes in continental-scale temperature and the hydrologic cycle, extremes events, and storm tracks, as well as regional manifestations of these climate variables. We also examine changes in eastern north Pacific and north Atlantic tropical cyclone activity and North American intraseasonal to decadal variability, including changes in teleconnections to other regions of the globe. Projected changes are generally consistent with those previously published for CMIP3, although CMIP5 model projections differ importantly from those of CMIP3 in some aspects, including CMIP5 model agreement on increased central California precipitation. The paper also highlights uncertainties and limitations based on current results as priorities for further research. Although many projected changes in North American climate are consistent across CMIP5 models, substantial intermodel disagreement exists in other aspects. Areas of disagreement include projections of changes in snow water equivalent on a regional basis, summer Arctic sea ice extent, the magnitude and sign of regional precipitation changes, extreme heat events across the Northern U.S., and Atlantic and east Pacific tropical cyclone activity.

16. Sheffield, J, S J Camargo, R Fu, Qi Hu, X Jiang, Nathaniel C Johnson, K B Karnauskas, Seon Tae Kim, J L Kinter, Sanjiv Kumar, B Langenbrunner, E Maloney, A Mariotti, J E Meyerson, J D Neelin, S Nigam, Zaitao Pan, A Ruiz-Barradas, R Seager, Y L Serra, D-Z Sun, Chunzai Wang, Shang-Ping Xie, J-Y Yu, Tao Zhang, and Ming Zhao, December 2013: North American Climate in CMIP5 Experiments. Part II: Evaluation of Historical Simulations of Intra-Seasonal to Decadal Variability. Journal of Climate, 26(23), DOI:10.1175/JCLI-D-12-00593.1.
    Abstract

    This is the second part of a three-part paper on North American climate in CMIP5 that evaluates the 20th century simulations of intra-seasonal to multi-decadal variability and teleconnections with North American climate. Overall, the multi-model ensemble does reasonably well at reproducing observed variability in several aspects, but does less well at capturing observed teleconnections, with implications for future projections examined in part three of this paper. In terms of intra-seasonal variability, almost half of the models examined can reproduce observed variability in the eastern Pacific and most models capture the midsummer drought over Central America. The multi-model mean replicates the density of traveling tropical synoptic-scale disturbances but with large spread among the models. On the other hand, the coarse resolution of the models means that tropical cyclone frequencies are under predicted in the Atlantic and eastern North Pacific. The frequency and mean amplitude of ENSO are generally well reproduced, although teleconnections with North American climate are widely varying among models and only a few models can reproduce the east and central Pacific types of ENSO and connections with US winter temperatures. The models capture the spatial pattern of PDO variability and its influence on continental temperature and West coast precipitation, but less well for the wintertime precipitation. The spatial representation of the AMO is reasonable but the magnitude of SST anomalies and teleconnections are poorly reproduced. Multi-decadal trends such as the warming hole over the central-southeast US and precipitation increases are not replicated by the models, suggesting that observed changes are linked to natural variability.

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