# ClimBurst: A Novel Method to Detect Climatological Anomalies Over Time and Space

Audrey Brouillet, Guillaume Coulaud, Dennis Shasha, Reza Akbarinia, Florent Masseglia

► To cite this version:

Audrey Brouillet, Guillaume Coulaud, Dennis Shasha, Reza Akbarinia, Florent Masseglia. ClimBurst: A Novel Method to Detect Climatological Anomalies Over Time and Space. Geophysical Research Letters, 2025, 52 (19), pp.e2025GL117095. 10.1029/2025gl117095. hal-05379952

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Distributed under a Creative Commons Attribution 4.0 International License
# Geophysical Research Letters®

## RESEARCH LETTER

10.1029/2025GL117095

**Key Points:**

* A new method, *ClimBurst*, is described that can detect climate bursts (anomalies) without prior knowledge of their duration

* Connected components analysis of detected bursts enables the tracking of spatio-temporally connected anomalies as they propagate in time and space

* As a case study, *ClimBurst* detects sea surface temperature hot anomalies, capturing their increased intensity in magnitude, frequency, duration and spatial extent

**Supporting Information:**

Supporting Information may be found in the online version of this article.

**Correspondence to:**

A. Brouillet, audrey.brouillet@pik-potsdam.de

**Citation:**

Brouillet, A., Coulaud, G., Shasha, D., Akbarinia, R., & Masseglia, F. (2025). *ClimBurst*: A novel method to detect climatological anomalies over time and space. *Geophysical Research Letters*, *52*, e2025GL117095. https://doi.org/10.1029/2025GL117095

Received 16 MAY 2025

Accepted 18 JUL 2025

**Author Contributions:**

**Conceptualization:** Audrey Brouillet, Guillaume Coulaud, Dennis Shasha, Reza Akbarinia, Florent Masseglia

**Data curation:** Guillaume Coulaud

**Formal analysis:** Guillaume Coulaud

**Funding acquisition:** Reza Akbarinia, Florent Masseglia

**Investigation:** Audrey Brouillet, Guillaume Coulaud, Dennis Shasha, Reza Akbarinia, Florent Masseglia

**Methodology:** Audrey Brouillet, Guillaume Coulaud

**Project administration:**
Audrey Brouillet, Dennis Shasha, Reza Akbarinia, Florent Masseglia

**Resources:** Florent Masseglia

© 2025. The Author(s).

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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## ClimBurst: A Novel Method to Detect Climatological Anomalies Over Time and Space

Audrey Brouillet¹,² ID, Guillaume Coulaud³ ID, Dennis Shasha⁴ ID, Reza Akbarinia⁴, and Florent Masseglia⁴

¹ESPACE-DEV, Université Montpellier, IRD, Université Guyane, Université Réunion, Université Antilles, Montpellier, France, ²Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany, ³Inria, CNRS, LIRMM, University of Montpellier, Montpellier, France, ⁴Courant Institute of Mathematical Sciences, New York University, New York, NY, USA

**Abstract** Detecting abnormal climate events across temporal and spatial scales is crucial to the understanding of local and regional climate trends. Existing methods often depend on prior knowledge about the timing, location, or duration of such events, limiting their versatility. This study introduces *ClimBurst*, an approach to detect climate bursts (unusually high or low values of climate variables) without prior assumptions about their temporal duration. *ClimBurst* offers the ability to: (a) identify climate bursts of any duration within the time series of single locations, (b) link climate bursts across neighboring locations, and (c) analyze the spatio-temporal propagation of these anomalies. Applying *ClimBurst* to sea surface temperature data from the Mediterranean Sea (1960–2021) shows some detected hot bursts and anomalies coincide in time with known severe marine heatwaves. *ClimBurst* also shows how detected hot (cold) bursts are spatio-temporally connected and this connected bursts have increased (decreased) in duration, intensity, spatial extent and frequency historically.

**Plain Language Summary** Detecting abnormal climate events is crucial for understanding, predicting, and managing climate risks. However, most existing methods require prior knowledge about when and where to search for these events, limiting their effectiveness. In this study, we introduce *ClimBurst*, a new method to identify climate-related anomalies that does not require any prior information about their duration or spatial extent. We propose computing climate bursts to detect abnormal seasonal activity. The *ClimBurst* approach can detect anomalies at any time scale. The approach also compares anomalies at neighboring locations enabling the tracking of events across time and space. We apply our method on sea surface temperature data from the Mediterranean Sea between 1960 and 2021, where we detect particularly strong warm anomalies that can last from a few days to a few months over a few kilometers to hundreds, such as the 2015 marine heatwave. Our results reveal a noticeable increase in the frequency, magnitude and the spatial extent of these hot anomalies over time. Researchers and practitioners can use *ClimBurst* to detect and study climate anomalies, providing a basis for event attribution and long-term trend analysis.

### 1. Introduction

Climate researchers have identified the detection of extreme climate events and related anomalies as a topic requiring extensive research (Sillmann et al., 2017). Anomalous events can significantly affect various sectors including agriculture (Beillouin et al., 2020; Vogel et al., 2019), water availability (Schilling et al., 2020; Vinke et al., 2017) and health (Alcayna et al., 2022; Bell et al., 2018; Charlson et al., 2021; Javadinejad et al., 2020). To cite a few recent examples from the literature of related studies, authors of Beillouin et al. (2020) use agricultural yield and climate data from 17 European countries to demonstrate that negative yields result from extremes in temperature and precipitation. In Alcayna et al. (2022), the authors conducted a scoping review of the relationship between climate-sensitive infectious diseases and climate extremes. In particular, they show an association between water-borne diseases and extremes. These two examples illustrate the necessity of accurately detecting and anticipating extreme events.

Extreme weather and climate events are usually defined as “discrete episodes of extreme weather or unusual climate conditions, often associated with deleterious impacts on society or natural systems, defined using some metric to characterize either the meteorological characteristics of the event or the consequent impacts” (Stott et al., 2016). As human-driven greenhouse gas emissions have led to global warming that results in more frequent
**Software:** Guillaume Coulaud
**Supervision:** Audrey Brouillet, Dennis Shasha, Florent Masseglia
**Visualization:** Guillaume Coulaud
**Writing – review & editing:**
Audrey Brouillet, Guillaume Coulaud, Dennis Shasha, Reza Akbarinia, Florent Masseglia

and intensified extreme events (Gulev et al., 2021), it has become crucial to reliably detect and study such possible risks (Chen et al., 2018). Many previous studies have developed related detection approaches using statistical and dynamic modeling. One common way to characterize and investigate climate-related anomalies is, for instance, the use of standardized indexes. These indexes transform raw observations into normalized data (mostly relative to their means) making them easier to interpret. For instance, the Standardized Precipitation Index (SPI) quantifies observed precipitation as a standardized departure from a selected probability distribution function that models the raw precipitation data (McKee et al., 1993; Tsakiris & Vangelis, 2005). Quantitatively, SPI values are the number of standard deviations by which the observed anomaly deviates from the long-term mean. The SPI is usually created using monthly input values and for differing periods from 1 to 36 months. A large number of standardized indexes to investigate climate-related anomalies (droughts, floods, vegetation anomalies, combined temperature-precipitation) have now been developed and adapted from this original index (Şen, 2024). Abrupt or extreme climate events can also be identified in spatio-temporal data by using edge detectors (Bathiany et al., 2020). Other approaches identify anomalous events using clustering, particularly when focusing on individual days and point anomalies (Ramachandra et al., 2016; Zscheischler et al., 2013).

Existing tools usually identify climate extremes by computing the percentile of each month-day-year date for each measure (e.g., temperature) relative to the same month-day in other years. If the percentile for a given day is very high or very low, then that day is considered extreme. For example Messori et al. (2024), define extremes as exceeding the percentile threshold (e.g., 95) at specific grid points, and a subsequent clustering step is used to determine the spatial extent of heatwaves or cold spells. Similarly (Yin et al., 2025), present a method that combines percentile-based thresholds with absolute criteria (e.g., days over a certain temperature) to identify compound events. Different strategies are employed to manage seasonality and mitigate potential biases (Brunner & Voigt, 2024). Nevertheless, these existing methods either require prior assumptions about thresholds to detect an anomaly and/or an extreme and don't address linking events spatio-temporally (Brunner & Voigt, 2024), rely on predefined climate variables types, timing, and calculated indicators that do not emphasize generic anomalies and propagation (Yin et al., 2025), database such as Klein et al. (2002), are not fully unsupervised or able to track anomalies propagating across space (Tilloy et al., 2022), or require users to predefine a percentile (e.g., 95th for heatwaves), minimum duration, and geographic clustering method, meaning results depend heavily on such (supervised) choices (Messori et al., 2024).

An event evolves in its own way and is, in principle, attributable to a unique set of drivers (Stott et al., 2016). Many studies assume prior knowledge regarding the time period and spatial extent of each anomalous event, resulting in a strong dependency of each detection method on a prior knowledge of its event type. For instance, to assess whether a given trend or event is statistically attributable to global warming, the first steps often include fixing the spatial and temporal definition of the given event and the parameters to be analyzed (Eden et al., 2016, 2018; Otto et al., 2023; Zachariah et al., 2023). The physical parameters chosen for such studies are usually the most extreme and abnormal ones (Philip et al., 2020). Further, attribution of an event requires a generalized ability to identify anomalies in any variables that might influence an event (Stott et al., 2016).

Understanding how specific climate anomalies propagate over space is also crucial for predicting where and when resulting disasters may occur. Many tracking approaches have been employed to analyze propagation in various climate impact sectors including (but not limited to) tropical cyclones (Knutson et al., 2019), storms, floods, or flash droughts (J. Li et al., 2020). Related studies usually address these issues by detecting spatial patterns that describe spatial boundaries of the given anomalous event. Such detection methods often use spatio-temporal clustering approaches such as connected components (Tilloy et al., 2022), then show how related spatial clusters evolve in time and space by identifying close events or behaviors occurring simultaneously.

In this study, we introduce *ClimBurst*, a new method for detecting climate anomalies. What strongly distinguishes ClimBurst from previous work is that (a) it operates without fixed thresholds or seasonal windows (Brunner & Voigt, 2024; Messori et al., 2024; Yin et al., 2025), allowing it to adaptively detect bursts of any duration; (b) it can identify bursts that are extreme over their full duration even if no single day is extreme; and (c) because bursts can have any duration, there is no need to predefine the duration of anomalies, unlike existing studies (Eden et al., 2016, 2018; Otto et al., 2023; Zachariah et al., 2023). Additionally, ClimBurst explicitly reveals the spatio-temporal connectivity and propagation dynamics of detected bursts, linking them across space while tracking how their intensity, duration, and spatial extent evolve over time.
## 2. Material and Methods

### 2.1. Climate Bursts to Detect Climate Anomalies

In this study, a burst (or climate burst) is defined as a time interval starting in a given year y whose accumulated values are unusually high or low for a given climate variable (e.g., +5°C temperature anomaly from 3 July to 16 July 2004) compared to the same time period in all years. Our approach identifies bursts over all possible time intervals, regardless of their duration or season, enabling the detection of short events (e.g., a 3-day burst in April of some year) as well as longer anomalies (e.g., a 15-day burst from 26 December of year y to 10 January of year y + 1).

Computationally, a burst is characterized by its z-score (similarly to the SPI, Tsakiris & Vangelis, 2005) that quantifies the relative position of an individual data point within a distribution. This score represents the number of standard deviations by which a data point deviates from the mean of the distribution. The higher the absolute z-score value of a data point, the more anomalous it is. We use z-score instead of percentiles for the following reason: with percentiles, for every month-day there will be one or more years which have a high percentile on that month-day and at least one or more years which are low on that month-day. With z-scores, there will be many month-days without any year having a z-score over some bound. For example, a uniform distribution of temperatures will have no values whose z-scores fall above 2 or below -2. Thus, using the z-score allows us to detect events that are really extreme, not just those that lie in the tail of the distribution.

The z-score is calculated using the well-known formula (Larsen & Marx, 2005):

$$z = \frac{x - \mu}{\sigma}$$

where z is the z-score, x the observed data value (e.g., the sum of the temperatures over a certain date range), μ the mean of the distribution (e.g., of sums), and σ the standard deviation.

As an illustrative climate example, the z-score of rainfall in Dakar over a certain time interval (e.g., 8 July to 23 July) of a certain year (e.g., 2004) would be the z-score of the total rainfall over that period in Dakar based on the total rainfall in Dakar between 8 July to 23 July in each year on record. The z-score provides a contextual measure of abnormality based on the typical values over the date range. This reflects the fact that the severity of an anomaly is often contextual.

#### 2.1.1. Overview of ClimBurst Algorithm

For all interval lengths within a user-defined range (e.g., 3–90 days), ClimBurst:

* Computes the cumulative value (e.g., sum of temperature) for every interval;

* For each calendar interval (defined by starting and ending month-dates), gathers values from the same calendar period across all years in the data set;

* Computes the mean and standard deviation for that calendar-specific distribution;

* Calculates the z-score of the interval value relative to the distribution of that calendar period across years;

* Returns intervals whose z-scores exceed a user-defined positive or negative z-score threshold (e.g., ±3).

Although computing bursts across all intervals length (duration) might seem computationally expensive, we provide an algorithm that does it quickly. For example, for a 64-year time series of daily temperature data, with an absolute z-score threshold of 3, we find all bursts of duration 1 to 365 in under 50 ms on a conventional laptop [Intel i9-13900H@2.6 GHz]. Further algorithmic details are available in Supporting Information S1 (see Text S1).

Given that we can identify climate bursts of any duration, some of these may be included in, or exhibit a high level of overlap with one another. This leads to a considerable degree of redundancy. We merge them into continuous sequences, forming a *Merged Burst Representation* (e.g., merging overlapping bursts from 3–16 July and 10–19 July into a single event spanning 3–19 July). This process takes under 10 ms for a single time series on the same hardware platform.
## 2.2. Climate Bursts and Spatial Extents: Connected Components

In this study, we combine burst detection at individual grid cells with connected component analysis to identify patterns of neighboring bursts and how they evolve in time and space. This makes it possible to differentiate between small-scale anomalies (e.g., local rainfall event) and larger-scale processes (e.g., seasonal monsoon) (C. Li et al., 2019).

After applying the burst detection algorithm to each grid cell, ClimBurst computes connected components based on spatio-temporal adjacency of the bursts. Specifically, a burst in one grid cell is considered adjacent to another if: (a) the two grid cells are adjacent in space using a 8-neighbor connectivity, (b) their burst time intervals have at least one overlapping day.

## 2.3. Case Study on Sea Surface Temperatures (SST) in the Mediterranean Sea

ClimBurst can be applied to any spatial scale (global to local) and on any climate-related variables. This paper presents a case study on one variable and one data set to illustrate how the method can be used and what information can be extracted. We apply ClimBurst to daily non-detrended sea surface temperature (SST) from the fifth-generation reanalysis produced by the Copernicus Climate Change Service (C3S) at the European Center for Medium-Range Weather Forecast (ECMWF), hereafter ERA5. This data set combines model data with global-scale observations (i.e., data assimilation) into a consistent and complete climate data set (Hersbach et al., 2020).

We focus on SST over the Mediterranean Sea (coordinates: 30°N to 46°N, -5°E to 36°E) and apply our method on the regional averaged SST. As ClimBurst can detect all z-scores bursts, here we display and describe main results based on bursts with an absolute z-score value greater than 3 (see more z-score thresholds in Figure S1 in Supporting Information S1). We argue such z-score value of 3 is a commonly used value to detect outliers with z-score methods (Iglewicz & Hoaglin, 1993). In this paper, positive z-score values correspond to *hot/warm* anomalies while negative values correspond to *cold* anomalies. For this paper, we also only consider events of small (1 day) to medium (3 months) temporal duration for simplification purpose, but please note that ClimBurst is not limited to these scales.

# 3. Results

## 3.1. Regional Averaged SST Bursts Within the Mediterranean Sea

On the average SST calculated over the Mediterranean sea, we identify five distinct bursts of varying duration in 2001, 2015, 2016, and two in 2018 (Figure 1a). These bursts represent statistical anomalies when compared to the same date ranges between 1960 and 2021. These climate bursts range from short events lasting 2–4 days in 2015 and 2018, to longer ones of 15 and 30 days in 2016 and 2001, respectively, and three of these five bursts are detected during spring (Figure 1a). Since the anomalies are calculated over the entire averaged Mediterranean basin, they detect large scale events, such as the severe marine heatwave of 2015 (Darmaraki et al., 2019).

Our climate burst approach can also provide daily absolute maximum z-scores information. Since a single day can belong to intervals of varying durations with different z-score values, we assign to each day the highest absolute z-score among those intervals. This reveals how the highest intensities of bursts are distributed across years. We display such information similarly as a Hovmöller diagram (Figure 1b). Along the dates of a year, the daily values exhibit various maximum z-score values across years, with the five highest values for SST in 2001, 2015, 2016, and 2018 (consistent with Figure 1a). This diagram also reveals how this method detects particular long hot events (e.g., hot SSTs during the extended summer in 2003) while z-scores are not the highest. Consistently with the literature (Nykjaer, 2009), corresponding burst have intensified and are predominantly positive (hot) up to 2021, with no negative (cold) anomalies since the winter of 2008.

Figure 1c shows the z-score distribution for the year 2001 for exponentially increasing numbers of days (see more years in Figure S2 in Supporting Information S1). In this figure, we illustrate intervals whose durations (in days) are powers of two (arbitrarily chosen for visual simplicity) and calculate their z-scores. In the example, the highest z-score bursts are detected at the end of March 2001 (black lines show z-scores above +3). These bursts seem to be included in a longer 32-day scale anomaly in March-April with a lower yet still elevated z-score. The exponential analysis enables the detection of climate-related anomalies covering multiple time scales. This shows the linkage between short time-scale events and long time-scale anomalies.
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Detected bursts in sea surface temperature (SST) over the Mediterranean sea during the 1960–2021 period. (a) Seasonal cycles of averaged Mediterranean SST during 1960–2021 and detected bursts (colored lines) with absolute z-score value above 3. Each color indicates a particular year in which a burst occurred; (b) Heatmap of daily maximal z-scores over 30 years. Positive (hot) bursts are displayed in red whereas negative (cold) bursts are shown in blue; (c) Exponentially scaled visualization of z-scores for 2001.</figcaption></figure>

## 3.2. Spatio-Temporal Connected Bursts (CB)

We illustrate a specific detected spatio-temporal anomalous event lasting from 17 April 2013 to 27 May 2013 to describe characteristics of such detected connected burst (Figure 2a, see more examples from Figures S3–S6 in Supporting Information S1). To capture the evolution of the connected component's spatial extent, we define the density as the ratio of grid points with active bursts on a given day to the total grid points in the connected component. In the given example, the density begins to rise on 22 April (Figure 2b) and peaks around 3 May reaching 90% of the total number of grid points in the connected component. Spatially, the points containing the highest number of days belonging to bursts (≥30 days) are concentrated near the center of the pattern which is west of Crete (Figure 2c). Points having approximately 20 burst days are near the west and south-east of Greece, and the points with approximately 5–10 burst days are located near the edges of the connected component (Figure 2c). Figure 2d shows that the event started in the connected component center on 17 April, and then propagated north-westward and eastward in the following days. The grid points furthest from the center are the bursts that started the latest and stopped the earliest (Figures 2d and 2e). The latest occurrence within the pattern is the localized center near the West of Crete, which has the highest total number of burst days (Figures 2e and 2c).

ClimBurst here enables the capture and detailed description of the spatio-temporal evolution and propagation of climate-related anomalous events, regardless of their intensity, spatial extent, or time duration. As a complement to the connected component approach, we illustrate corresponding examples of *time travel* videos published on Github (https://github.com/GuillaumeCld/ClimBurst, see the Data Availability Statement Section). This visualization displays day-by-day and grid-by-grid information about this same climate event for illustration purposes.
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Spatio-temporal connected bursts (CB) related characteristics: example of an event detected in April-May 2013. (a) Spatial extent of the CB example; (b) Density (fraction of grid cells) in the CB that exceeded the z-score threshold of three over time; (c) Number of days per grid cell within the CB with a z-score of at least 3; (d) First date per grid cell being included in the CB; (e) Last date per grid cell being included in the CB.</figcaption></figure>

### 3.3. Trends of Connected Bursts Over 1960–2021

Here we investigate time evolution of detected connected SST bursts between 1960 and 2021 within the Mediterranean Sea (Figure 3, Figures S7 and S8 in Supporting Information S1). We show that the total number of days
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Evolution of cold and hot sea surface temperature bursts (absolute z-score above 3) within the Mediterranean Sea between 1960 and 2021. (a) Timeseries in the form of histogram of the aggregated number of days belonging to bursts per year for all grid cells. (b) Timeseries in the form of histogram of percentage of grid cells containing at least one burst day per year; (c) Bubble plots of the evolution of connected bursts (CB) features. Each bubble gives combined information of the year of occurrence (x-axis), the duration (y-axis), the spatial extent (bubble size), and the averaged CB temperature anomaly. If there are several bubbles in the same year, then there are several connected components in that year.</figcaption></figure>

included in a warm (cold) burst with z-score above (below) 3 (–3) significantly increases (decreases) over time between 1960 and 2021. Only a few days with hot bursts are found at the beginning of the 1960s followed by cold bursts from 1970 to 2000, and an increasing number of warm burst days from the early 2000s until 2021 (Figure 3a). Accordingly, the spatial extent (expressed in percentage of the number of grid cells over the number of all grid cells with at least one warm burst day per year) increases (Figure 3b). The method also shows that the cold burst spatial extent rarely exceeds 20%, whereas hot burst extent exceeds 20% most of the time after 2000.

The combined information regarding the four main averaged characteristics (spatial extent, duration, frequency of occurrence, and intensity) of connected bursts (CB) are displayed per year in the form of bubble plots (Ouzeau et al., 2016). For the sake of visual clarity, we have removed events lasting less than 4 days or covering less than 1% of the grid. Over the Mediterranean Sea, days and grid cell numbers included in a warm CB have both increased (Figure 3c). Consistently only few cold CB are detected from 2008 on (Figure 3c).

Trends are explored over the 1960–2021 period (Figures S7 and S8 in Supporting Information S1). Calculated trends over the period 1994–2021 indicate an increase of 2.24 hot CB events per decade, a rise of 1.75 days per decade in their duration, an intensification of 0.16 K per decade in their temperature anomaly (intensity), and an expansion of 0.56 grid cells per decade in their average spatial extent (Figures S7a–S7g and S8 in Supporting Information S1). Consistently, cold CB features have decreased (Figure S7b–S7h in Supporting Information S1). Our results are consistent with existing literature showing an observed mean warming of +0.4°C/decade in the Mediterranean Sea (Cherif et al., 2020). Our detected burst results are also consistent with detected strong marine heatwaves that occurred during the 2010s in the region (Darmaraki et al., 2019).
<figure><img src="image_6.png" /><figcaption>**Figure 4.** Heatmaps illustrating how many times a pixel is characterized as the first day of a climate burst with a z-score greater than 3 during 1960–2021. The relative threshold indicates the threshold to consider to select the first days of a connected component, here 5% in (a) and 20% in (b).</figcaption></figure>

**3.4. Hotspots of the Starting Points of Connected Bursts**

As an example of a possible use of ClimBurst, we investigate where warm spatial anomalies originate. To do this, for each connected component, we identify the grid cells whose burst begins in the first few days of the component. Two relative (quantile) thresholds of starting dates are represented in Figure 4 (first 5% days and first 20%). Heatmaps of all burst onsets of warm SST connected components reveal several localized hotspots (Figure 4). The figure shows that the Adriatic Sea, the Aegean Sea, and the Central Mediterranean are characterized by 6–8 hot connected component onsets. Literature has revealed that the maximum SST increase (+0.16°C/year) is found close to the African coast, in the Tyrrhenian, Ligurian, Adriatic, and Aegean Seas (Nykjaer, 2009). ClimBurst detected hotspots of onsets consistently with the literature except for in the Aegean and Adriatic seas, suggesting that local SST warm anomalies in those places are less related to the observed Mediterranean warming than warming in other locations.

**4. Conclusions and Discussion**

This study has introduced the *ClimBurst* method as a novel framework for identifying and analyzing anomalies in individual grid points of any time duration. Through an extension based on connected components, we demonstrated its analytical insights over space as well as time.

We have illustrated the analytical utility of the method by applying it to Sea Surface Temperature (SST) data from the Mediterranean Sea (1960–2021) obtained from the ERA5 data set. Our results showcase the ability of ClimBurst to detect contextual anomalies at various time scales and varying intensities (z-scores), independently of seasonal patterns. For example, anomalies are identified in both spring and summer. Warm SST anomalies align with known marine heatwaves in the region. Furthermore, we show that ClimBurst can track the spatio-temporal evolution of anomalies, identify their approximate local starting point, and analyze regional trends over the study period. This includes describing the evolution of the anomalies' features (spatial extent, duration, frequency).

Because climate bursts are contextual in that they are based on date ranges within a specific year range, they should be interpreted with caution. Anomalies with comparable z-scores across disparate time periods may differ significantly in terms of causes or impact. For example, hot anomalies in winter and early spring can result in more abrupt and earlier snow recession in some regions, whereas heat events in spring and summer mostly induce increased wildfire frequency and severity (Collow et al., 2022). ClimBurst thus should be explored and applied to other data sets, variables, and timescales, but this will not always be straightforward. For instance, when analyzing rainfall data, cumulative values for short intervals may be zero in the majority of years and non-zero in a few due to the inherently strong day-to-day variability (Sivakumar, 2001). In such cases, z-scores may erroneously classify non-zero intervals as outliers, potentially leading to false discoveries. These situations may thus require special-purpose post-processing such as focusing only on intervals above a certain number of days for rainfall.
Alternatively, percentile-based approaches, which are distribution-independent, may be more appropriate for highly skewed variables like precipitation. These methods evaluate extremeness based on rank rather than deviation from the mean, and may reduce the likelihood of false positives when the distribution is non-Gaussian (Messori et al., 2024). However, because ClimBurst compares intervals of fixed length and calendar date across multiple years, some of these small-sample distributions may lack a meaningful notion of extremeness, making percentile cutoffs less informative in certain cases. Future work might indeed explore integrating percentile-based analysis into ClimBurst, although special care must be taken to avoid false positives.

The connected components approach may link spatio-temporal anomalies into large components that contain distinct smaller-scale anomalies. While this offers a comprehensive perspective of interconnected phenomena, it may also link disparate or unrelated time series. Such issues should be explored in further studies to possibly classify anomaly types (e.g., disparate vs. connected broad anomalies) when using ClimBurst. The results can serve as a step toward event detection in attribution studies, where a generalized and systematic approach for event detection is often lacking (Liu et al., 2023; Van Der Wiel et al., 2017; Van Oldenborgh et al., 2021). This is particularly important when investigating whether a given event or trend is more or less probable due to human-induced global warming.

## Data Availability Statement

The sea surface temperature data are obtained from the ERA5 hourly data from the Copernicus Climate Change Service (C3S, 2018). We make the *ClimBurst* method available as an open-source code. The code consists of Jupyter notebooks with Python code to display the visualizations of the paper including year-duration-extent bubble plots, connected component evolutions, and time travel videos to visualize the temporal evolution of bursts at the grid or at a connected component level. The code is licensed under CeCILL and published on GitHub https://github.com/GuillaumeCld/ClimBurst. The GitHub also includes the averaged time series and the script to pre-process the grid.

## Acknowledgments

This work is supported by the Région Occitanie, AAP EMERGENCE 2023, Projet DACLIM. Open Access funding enabled and organized by Projekt DEAL.

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