Time of emergence in regional precipitation changes: an updated assessment using the CMIP5 multi-model ensemble

Nguyen, Thuy-Huong; Min, Seung‐Ki; Paik, Seungmok; Lee, Dong-Hyun

doi:10.1007/s00382-018-4073-y

Cited by 32 publications

(32 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most common application of ToE is to examine projected changes in temperature under climate change (Diffenbaugh and Scherer 2011, Mahlstein et al 2011, 2012a, Hawkins and Sutton 2012, Mora et al 2013). There are additional examples that consider other climate change factors such as projected changes in precipitation (Giorgi and Bi 2009, Mahlstein et al 2012b, Douglas 2013, Sui et al 2014, Nguyen et al 2018, projected changes in the frequency and intensity of climate extremes (King et al 2015, Bador et al 2016, Lee et al 2016, Tan et al 2018), projected changes in sea level (Lyu et al 2014), and projected changes in width of the earth's tropical belt (Quan et al 2018). A common method for estimating ToE is to identify the year in which the ratio between climate change and historical climatic variability (signal-to-noise ratio) first crosses a predefined threshold, such as one or two (Hawkins and Sutton 2012).…”

Section: Introductionmentioning

confidence: 99%

Time of emergence of novel climates for North American migratory bird populations

2019

View full text Add to dashboard Cite

To better understand the ecological implications of global climate change for species that display geographically and seasonally dynamic life‐history strategies, we need to determine where and when novel climates are projected to first emerge. Here, we use a multivariate approach to estimate time of emergence (ToE) of novel climates based on three climate variables (precipitation, minimum and maximum temperature) at a weekly temporal resolution within the Western Hemisphere over a 280‐yr period (2021–2300) under a high emissions scenario (RCP8.5). We intersect ToE estimates with weekly estimates of relative abundance for 77 passerine bird species that migrate between temperate breeding grounds in North America and southern tropical and subtropical wintering grounds using observations from the eBird citizen‐science database. During the non‐breeding season, migrants that winter within the tropics are projected to encounter novel climates during the second half of this century. Migrants that winter in the subtropics are projected to encounter novel climates during the first half of the next century. During the beginning of the breeding season, migrants on their temperate breeding grounds are projected to encounter novel climates during the first half of the next century. During the end of the breeding season, migrants are projected to encounter novel climates during the second half of this century. Thus, novel climates will first emerge ca 40–50 yr earlier during the second half of the breeding season. These results emphasize the large seasonal and spatial variation in the formation of novel climates, and the pronounced challenges migratory birds are likely to encounter during this century, especially on their tropical wintering grounds and during the transition from breeding to migration. When assessing the ecological implications of climate change, our findings emphasize the value of applying a full annual cycle perspective using standardized metrics that promote comparisons across space and time.

show abstract

Section: Introductionmentioning

confidence: 99%

Time of emergence of novel climates for North American migratory bird populations

2019

View full text Add to dashboard Cite

show abstract

“…Hawkins and Sutton (2012) define time of emergence (ToE) as the time at which the signal of climate change emerges from the noise of natural variability. ToE studies not only focus on precipitation (Giorgi and Bi 2009;Hawkins and Sutton 2011;Lee et al 2016;Nguyen et al 2018) and temperature (Diffenbaugh and Scherer 2011;Hawkins and Sutton 2012;Mahlstein et al 2011;Mora et al 2013), but also heat waves and extreme temperatures (Harrington et al 2016;King et al 2015;King et al 2016;Lopez et al 2018), sea level (Carson et al 2016;Lyu et al 2014), current system upwelling (Brady et al 2017), and different ocean properties (Keller et al 2014;Henson et al 2017). Finding ToE is relevant as it serves as a marker of human-induced climate change.…”

Section: Introductionmentioning

confidence: 99%

“…Finding ToE is relevant as it serves as a marker of human-induced climate change. Recent studies also mention its importance for decision-making, especially when implementing adaptation and mitigation plans (Nguyen et al 2018;Zhou et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Estimating the Local Time of Emergence of Climatic Variables Using an Unbiased Mapping of GCMs: An Application in Semiarid and Mediterranean Chile

Chadwick

Gironás

Vicuña

et al. 2019

Journal of Hydrometeorology

View full text Add to dashboard Cite

The time at which climate change signal can be clearly distinguished from noise is known as time of emergence (ToE) and is typically detected by a general circulation model (GCM) signal-to-noise ratio exceeding a certain threshold. ToE is commonly estimated at large scales from GCMs, although management decisions and adaptation strategies are implemented locally. This paper proposes a methodology to estimate ToE for both precipitation and temperature at local scales (i.e., river basin). The methodology considers local climatic conditions and unbiased GCM projections to estimate ToE by using the statistical power to find when the climate significantly differs from the historical one. The method suggests that ToE for temperature already occurred in three Chilean basins (Limarí, Maipo, and Maule). However, in terms of precipitation, an earlier ToE is clearly identified for the Maule basin, indicating that risk assessment and adaptation measures should be implemented first in this basin.

show abstract

“…The greenhouse gas scenario sensitivity is measurable through trends in observed time series (Madsen et al, 2014). In climate model data, it is shown by the temporal evolution of the predictor changes (Maraun, 2013;Nguyen et al, 2018) and a comparison of the predictor changes for different representative concentration pathways (RCPs) (Jacob et al, 2014;KNMI, 2014;Van Uytven and Willems, 2018).…”

Section: Introductionmentioning

confidence: 99%

Uncovering the shortcomings of a weather typing based statistical downscaling method

Uytven

Niel

Willems

2019

Preprint

View full text Add to dashboard Cite

Abstract. In recent years many methods for statistical downscaling of the climate model outputs have been developed. Each statistical downscaling method (SDM) has strengths and limitations, but those are rarely evaluated. This paper proposes an approach to evaluate the skill of SDMs for the specific purpose of impact analysis in hydrology. The skill is evaluated by the verification of the general statistical downscaling assumptions, and by the perfect predictor experiment that includes hydrological impact analysis. The approach has been tested for an advanced weather typing based SDM and for impact analysis on river peak flows in a Belgian river catchment. Significant shortcomings of the selected SDM were uncovered such as biases in the frequency of weather types and non-stationarities in the extreme precipitation distribution per weather type. Such evaluation of SDMs becomes of use for future tailoring of SDM ensembles to end user needs.

show abstract

Time of emergence in regional precipitation changes: an updated assessment using the CMIP5 multi-model ensemble

Cited by 32 publications

References 46 publications

Time of emergence of novel climates for North American migratory bird populations

Time of emergence of novel climates for North American migratory bird populations

Estimating the Local Time of Emergence of Climatic Variables Using an Unbiased Mapping of GCMs: An Application in Semiarid and Mediterranean Chile

Uncovering the shortcomings of a weather typing based statistical downscaling method

Contact Info

Product

Resources

About